L Gutman

J R Kennedy

F Roch

A Marceaux Dit Clément

L Salsi

J Cauzid

B Rouat

H Combeau

M Založnik

J Zollinger

Characterisation of mesosegregations in large steel ingots

The chemical composition mapping of low-alloyed steel ingots used for the nuclear industry is crucial in the manufacturing of forgings and their final quality mastering. Mechanical properties of forged and hot-rolled steels may be affected by chemically segregated bands. These bands arise from segregations that appear at the scale of a few grains in the as-cast structure: the so-called mesosegregations. While segregation at the scale of dendrite arms (microsegregation) and the scale of the casting (macrosegregation) is well understood and can be readily characterized, only little is known about the formation of mesosegregation. The first step towards understanding the cause behind mesosegregation formation can be brought through comprehensive chemical characterisation at the scale of several grains (mesoscopic scale), which requires using different characterisation techniques compared to micro-or macrosegregation characterisation. We developed a sampling and characterisation methodology that allows segregations to be mapped at the mesoscopic scale using micro X-ray fluorescence (µXRF). Characterisation technique, sampling methodology, and sample size must be adapted to consider the different solidification structures; both at smaller (dendrite arms, grains) and larger (macrostructure) scales. Segregations were characterised on a 113 x 98 mm² steel plate extracted from a low-alloyed steel large ingot.

Introduction

Segregation of alloying elements occurs as steel solidifies. In steel, the common alloying elements (C, Mn, Mo for instance) are more soluble in the liquid than in the solid [START_REF] Lesoult | Techniques de l'ingénieur[END_REF]. As steel solidifies, alloying elements are rejected into the liquid phase. This solute enrichment of the liquid phase leads to chemical heterogeneity in the solid-state. Such heterogeneity is called segregation and exists at different scales. Segregation is a departure from the nominal chemical composition of the steel, local enrichments cause positive segregations whereas local depletions cause negative segregations. Microsegregation is segregation observed at the dendrite scale (from µm to a few hundreds of µm), due to solute rejection during the growth of dendrites. It can be influenced by solute diffusion difference between the liquid and solid-state [START_REF] Dantzig | Solidification[END_REF]. Microsegregation creates depleted solid and enriched liquid that move at the ingot scale (m) through convection, movement of grains, solidification shrinkage and mushy zone deformation forming macrosegregation [START_REF] Pickering | [END_REF]. While heat treatment can reduce microsegregation through diffusion, macrosegregation cannot be reduced at the industrial scale since diffusion is too slow of a process. Thus macrosegregation cannot be reduced after solidification, it may remain in the semiproducts and even in the forged part thus affecting final mechanical properties [4].

Segregations that exist in the ingot influence solid-state transformations, which cause heterogeneities in microstructures and final mechanical properties. Forging steps change the size, shape, and distribution of segregations but do not decrease their intensity or number [5]. The change in shape and size of segregations causes a banding phenomenon in forged and hot-rolled steels: bands of different microstructures and compositions are observed [6]. Banding in steel leads to anisotropic mechanical properties. This is especially true for impact toughness properties which can be sensitive to orientation and depth of specimen sampling [7,8]. Banding is caused by segregations larger than the dendrite scale (microsegregations), but smaller than channels (macrosegregation) [9]. Such intermediate scale segregations, probably at the grain-scale, are called mesosegregations. To understand and influence banding phenomena, understanding the formation of mesosegregations is key.

A comprehensive characterisation of mesosegregations is the first step towards understanding their formation. Different characterisation techniques exist to track chemical heterogeneities at different scales. Investigation of microsegregation is commonly carried out with an Electron Probe Micro Analyser (EPMA) which enables the quantification of the chemical composition with an interaction volume of roughly 1 µm 3 . In large steel ingots, macrosegregation can be tracked with Optical Emission Spectrometry (OES) or infrared combustion analysis for carbon and sulphur investigation. Macrosegregation can also be detected by X-Ray Fluorescence (XRF) spectroscopy. Manganese (Mn) and Molybdenum (Mo) macrosegregation patterns can easily be distinguished, with a 5 mm analysis step and minimal sample preparation [10]. However, these techniques cannot be adapted to intermediatescale segregation in the case of large ingots due to their lack of spatial resolution. Large ingots used in the nuclear industry have long solidification times and so present large structures: investigated samples present secondary arm spacing of 800 µm. Hence to investigate the occurrence of mesosegregations in such ingots, chemical composition should be mapped at the centimetric grain scale. At this scale, EPMA analysis is impractical: too detailed and too time consuming. In contrast, the OES and combustion analysis techniques are too coarse to be implemented at a centimetric scale.

In this paper we developed a new sampling and characterisation methodology, suitable for the chemical analysis of mesosegregations in steel ingots. This new method is based on micro-X-Ray Fluorescence (µXRF), which has begun to be investigated as a tool to analyse centreline segregation during continuous casting [START_REF] Sengupta | AISTech 2017 Proceedings[END_REF][START_REF] Sengupta | AISTech 2018 Proceedings[END_REF]. First, the effects of sampling on the observed structures and recorded data were analysed in order to define sampling parameters applicable to a centimetric scale mapping. Then, calibration was conducted by comparison with EPMA to convert X-ray counts to quantitative concentration. Finally, a centimetric scale quantitative map was produced combining µXRF and EPMA calibration.

Material and methods

Samples

The low-alloy steel 16MND5 (0.16 %wt. C, 1.45 %wt. Mn, 0.725 %wt. Ni and 0.5 %wt. Mo) was selected for analysis. Samples were taken from the equiaxed region of an industrially produced 116 ton directionally solidified ingot. Samples were cut using wire electron discharge machining.

Micro-X-Ray Fluorescence (µXRF)

Micro-X-Ray Fluorescence (µXRF) analyses were performed with a Bruker M4 Tornado device equipped with a Rh X-ray source (30 W power). Two EDS (Energy Dispersive Spectroscopy) detectors are located on either side of the primary X-ray source. Both the source and the detectors are fixed in the vacuum chamber (20 mbar vacuum) whereas the samples are placed on a motorized stage. The spot size focused on the sample is 20 µm.

Low-alloyed steel samples of different sizes were used to investigate the effects of experimental conditions on the results. For µXRF measurements, a metallographic surface finish is not required, samples were ground flat with Si-C paper down to P800 (21.8 µm granulometry).

Concerning the primary X-ray source conditions, acceleration voltage, current, and the use of filters on the beam enable improvement of the signal and minimisation of artefacts. An indicator for artefact minimisation is the dead time, as dead time approaches 20 %, artefacts are minimised. Dead time can be managed by balancing current, acceleration voltage, and filters. Acceleration voltage was set to 50 keV for all analyses. Filters used on the X-ray beam are high-pass filters, they enable suppression of a part of the signal to avoid saturation and artefacts. However, strong filters can suppress peaks that correspond to elements of interest, for instance, the Manganese signal is affected by such strong filters. The use of filters can be optimised to approach 20 % of dead time without decreasing the current. This is beneficial as a low current leads to a weak outcoming signal.

Electron Probe Micro Analyzer (EPMA)

The Electron Probe Micro Analyser (EPMA) device used is a JEOL JXA-8530F equipped with a FEG source, an EDS and 5 WDS (Energy and Wavelength Dispersive Spectroscopy) detectors. The electron beam operates at 20 kV and 100 nA for the analysis of Mn and Mo on LIF and PET crystals respectively, calibrated with standards Pel6080-Mn and Pel6080-Mo. Samples analysed with EPMA require a mirrorlike surface and were polished with a 1µm diamond slurry.

Cumulative solid fraction curves

As solidification occurs the solute concentration in liquid increases (the partition coefficients of the studied elements are smaller than one). One way to compare the different characterisation techniques is to associate a solid fraction with data points and to plot the evolution of concentration (for EPMA analysis) or peak intensity (for µXRF analysis) as a function of the increasing solid fraction. The first solid to form is the most depleted and the last solid to form is the richest in solute. Intermediate data points are then ranked. Different ranking techniques exist and are used for microsegregation analysis, several of them are compared by Ganesan [START_REF] Ganesan | [END_REF]. In the present study, the data points are arranged monotonically into ascending order according to a single element (Flemings-Grungor method).

Experimental methodology development

Sampling effect

The X-ray beam of the µXRF device used in this study has a 20 µm width with a fixed focal point. With µXRF the sampling is not homogenous in both directions. The surface is scanned continuously in the x-direction, a single spectrum is obtained by collecting X-rays over a given time, as the stage translates at a constant speed this corresponds to a specific length, called the analysis step. In the y-direction, the scanning is discrete since the lines are spaced by a distance equal to the analysis step in order to maintain square pixels. A certain area between the lines, corresponding to the difference between the analysis step and the beam width (20 µm) is not analysed, as shown in the schematic in figure 1. The only way to map the whole surface is to use an analysis step equal to the beam width, 20 µm, in which case, there is no space between the analysed lines. Three representative maps are presented in figure 2. The visual aspects of the maps are affected by the sampling. With a 20 µm step, the surface is completely mapped, the edges of the structures are sharp, small microstructure details are detected. As the acquisition step increases, the structures seem to spread out and their shape can be distorted. With acquisition steps bigger than 100 µm the shape of the structure is no longer representative, as such, acquisition steps smaller or equal to 100 µm should be favoured. The cumulative solid fraction curves are presented for Mn and Mo in figure 3. The collected peak intensity increases with the analysis step for both elements. It is linked to an increase in acquisition time: the longer the detector collects the fluorescent X-rays, the greater the peak intensity. The intensity of the peaks corresponds to the acquisition time, but the curve shape is linked to the acquisition step. To isolate the influence of the acquisition step relative cumulative solid fraction curves are used. With a step of 20 µm, the whole surface is analysed whereas in the other cases it is not. The curves associated with analysis steps of 100 µm, 150 µm, and 200 µm are very similar. Increasing the analysis step does not seem to decrease the quality of the information needed for solid fraction curves. The benefit of small analysis steps such as 100 µm is questionable: it is time-consuming, and a 200 µm step can provide the same information. Using a small step can however be justified if mapping is of interest rather than cumulative segregation curves.

Quantification

µXRF analysis can provide chemical composition mapping at a centimetric scale. However, the initial map is expressed in counts, which correspond to the intensity of the peak associated with the element, and not its concentration itself. EPMA analysis, on the other hand, can provide quantified local concentrations, but cannot be used at a large scale. By combining these techniques, the µXRF centimetric scale mapping can be quantified.

To quantify the µXRF analysis, a 400-point grid of EPMA spot analyses were conducted on 3 different samples. A step size of 100 µm was used, hence the surface analysed was 1.9 x 1.9 mm². The same samples were then analysed with µXRF using the same experimental conditions as the centimetric mapping to be quantified. EPMA and µXRF data points were ranked in ascending order, then assuming that the greatest peak intensity corresponds to the greatest concentration and vice versa, EPMA data was plotted against µXRF data and a linear regression was performed on the curve. The regression equation can be applied to convert the peak intensities to concentrations.

Quantitative centimetric scale mapping

Based on previous observations, experimental conditions were defined to perform centimetric scale mapping. The analysed surface was 113 x 98 mm², the acquisition steps were 100 µm in both directions (resulting in 1 109 511 analysis points) and the analysis time was 45 ms per spectra. The X-ray beam operated at 50 keV and 200 µA, and a 100 µm thick Al filter was used to reduce dead time to 21 %. Once the data was collected, the quantification method was applied and quantitative centimetric scale maps obtained. The quantified map for Mn is presented in figure 4.

Mn is rejected into the liquid phase during solidification. Dendrite trunks and secondary arms can be distinguished on the quantified map. At the centimetric scale, the structures do not appear distorted confirming that our developed analysis protocol is suitable. The microsegregation is visible on the centimetric scale map. At a larger scale, two round spots of a few millimetres can be observed near the centre and at the bottom left corner of the map on both maps, which may be channel segregates. Also, the interdendritic spaces on the top right side of the map seem richer in Mn than in the centre of the map.

Conclusion

The understanding of mesosegregation formation requires chemical analysis at a centimetric scale. The objective of this work was the development of a new sampling and characterisation methodology that relies on µXRF and allows centimetric scale mapping. First, the effect of sampling on the observed structures was investigated to select suitable experimental conditions for centimetric scale mapping. As the surface scanning with µXRF techniques is anisotropic, a small acquisition step is employed to avoid shape distortion of the structures (it should be smaller or equal to 100 µm). The chemical composition of a 113 x 98 mm² sample was mapped with a 100 µm analysis step with the µXRF technique and quantified by calibration with EPMA. The chemical composition mapping reveals microsegregations and chemical heterogeneities at a larger scale. A geostatistical approach should be used on centimetric scale mappings to investigate mesosegregations patterns.

Figure 1 .

 1 Figure 1. Schematic of the surface scanning during µXRF analysis with different analysis steps. When the analysis step is 100 µm, a band of 80 µm is not analysed between each analysed line of 20 µm width. The EPMA grid discussed in paragraph 3.2 is also represented.

Figure 2 .

 2 Figure 2. Maps of Mn segregation ratio of a sample 4.4 x 5.6 mm² analysed with µXRF (X-ray beam: unfiltered, 50 keV, 200 µA) with different analysis step and time ((a) 20µm_20ms, (b) 100µm_100ms (c) 200µm_200ms).

Figure 3 .

 3 Figure 3. Classic (top) and relative (bottom) cumulative solid fraction curves (peak intensity as a function of the solid fraction) for µXRF data collection on a sample 4.4 x 5.6 mm², in the same experimental conditions (unfiltered, 50 keV, 200 µA) but with different analysis step and time (20µm_20ms, 50µm_50ms, 100µm_100ms, 150µm_150ms and 200µm_200ms). On the left is Mn peak intensity, on the right is Mo peak intensity.

Figure 4 .

 4 Figure 4. Map of Mn realised on a 113 x 98 mm² sample analysed with µXRF (X-ray beam: Al-100 µm, 50 keV, 200 µA), with a 100 µm analysis step and 45 ms of acquisition time per spectra. An enlarged view of the area delimited in red is shown on the right.

  Preprint version of published article. Cite as: L. Gutman et al., Characterisation of mesosegregations in large steel ingots, IOP Conference Series: Materials Science and Engineering, in press (2022)

Acknowledgments

This research work is supported by ANRT France under a CIFRE Ph.D. fellowship (Grant Number 2020/1234) and through the Industrial chair Solidification at Institut Jean Lamour, supported by ArcelorMittal, ArcelorMittal Industeel, CEA, EDF and Framatome.


Daniel Argüeso

Alejandro Di Luca

Nicolas C Jourdain

R Romero

V Homar

Mechanisms for extreme precipitation changes in a tropical archipelago

Keywords: Maritime Continent, Precipitation, Convection-Permitting, Climate Change, Convective parameterization, Extreme precipitation

The Maritime Continent is one of the most challenging regions for atmospheric models. Processes that modulate deep convection are poorly represented in models, which affects their ability to simulate precipitation features accurately. Thus, future projections of precipitation over the region are prone to large uncertainties. One of the key players in modelling tropical precipitation is the convective representation, and hence convection-permitting experiments have contributed to improve aspects of precipitation in models. This improvement creates opportunities to explore the physical processes that govern rainfall in the Maritime Continent, as well as their role in a warming climate. Here, we examine the response to climate change of models with explicit and parameterized convection and how that reflects in precipitation changes. We focus on the intensification of spatial contrasts as precursors of changes in mean and extreme precipitation in the tropical archipelago. Our results show that the broad picture is similar in both model setups, where islands will undergo an increase in mean and extreme precipitation in a warmer climate and the ocean will see less rain. However, the magnitude and spatial structure of such changes, as well as the projection of rainfall percentiles, are different across model experiments. We suggest that while the primary effect of climate change is thermodynamical and it is similarly reproduced by both model configurations, dynamical effects are represented quite differently in explicit and parameterized convection experiments. In this study, we link such differences to horizontal and vertical spatial contrasts and how convective representations translate them into precipitation changes.

Introduction

The Maritime Continent (Fig. 1) is the largest archipelago on Earth and one of the most active centers of deep moist convection on the planet. This tropical archipelago comprises thousands of islands of varied sizes and steep topography surrounded by the Indo-Pacific Warm Pool, a region with very high sea surface temperatures. Intense convective processes in the Maritime Continent (MC) shape its precipitation regimes and thus have direct implications locally. However, the scale and magnitude of convection is such that it helps transport large amounts of energy and moisture, modulating global circulation patterns [START_REF] Neale | The maritime continent and its role in the global climate: A GCM study[END_REF][START_REF] Yamanaka | Maritime continent coastlines controlling Earth's climate[END_REF]. Their intrinsic relationship with phenomena such as the Madden-Julian Oscillation (MJO) [START_REF] Birch | Scale Interactions between the MJO and the Western Maritime Continent[END_REF] and the Walker Circulation-ENSO [START_REF] Qian | Interactions among ENSO, the Monsoon, and Diurnal Cycle in Rainfall Variability over Java, Indonesia[END_REF] are key examples of the interaction across-scales that occur in the Maritime Continent.

The region has proven very challenging in terms of understanding and modelling precipitation characteristics and the associated physical processes. Despite the importance of the region at multiple scales, Global Climate Models fail to capture key features of the Maritime Continent such as the MJO propagation [START_REF] Peatman | Propagation of the Madden-Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation[END_REF][START_REF] Ling | Possible Role of the Diurnal Cycle in Land Convection in the Barrier Effect on the MJO by the Maritime Continent[END_REF] and the diurnal cycle of rainfall [START_REF] Baranowski | Contemporary GCM Fidelity in Representing the Diurnal Cycle of Precipitation Over the Maritime Continent[END_REF], largely due to their coarse resolution. In fact, even the most recent generation of GCMs (CMIP6) still show substantial biases in tropical precipitation [START_REF] Fiedler | Simulated Tropical Precipitation Assessed across Three Major Phases of the Coupled Model Intercomparison Project (CMIP)[END_REF]. As a result, even though the CMIPs multi-model means suggest increases in rainfall over the region (CMIP5: [START_REF] Jourdain | The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations[END_REF] and Supplementary Figure S1; CMIP6: Wang et al.

[2020]), individual GCMs strongly disagree in the sign of rainfall changes [START_REF] Jourdain | The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations[END_REF][START_REF] Narsey | Climate Change Projections for the Australian Monsoon from CMIP6 Models[END_REF].

Regional Climate Models (RCMs), which operate at higher spatial resolution, have contributed to improve our understanding and simulation of the mechanisms underlying rainfall in the Maritime Continent [START_REF] Vincent | Mesoscale Variation in Diabatic Heating around Sumatra, and Its Modulation with the Madden-Julian Oscillation[END_REF][START_REF] Ruppert | Diurnal forcing and phase locking of gravity waves in the maritime continent[END_REF][START_REF] Li | Assessing the role of the ocean-atmosphere coupling frequency in the western Maritime Continent rainfall[END_REF] through better representation of fine-scale processes (i.e., sea-breeze, gravity waves, interaction across scales, air-ocean fine-scale interactions). However, RCMs are still prone to substantial errors, partly originated from the interaction between the convective representation and the land-sea contrasts [START_REF] Birch | Sea-Breeze Dynamics and Convection Initiation: The Influence of Convective Parameterization in Weather and Climate Model Biases[END_REF][START_REF] Vincent | A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent[END_REF][START_REF] Im | Simulation of the diurnal variation of rainfall over the western Maritime Continent using a regional climate model[END_REF]. Models that explicitly represent convection bring improvements in the simulation of the precipitation diurnal cycle but produce unrealistic precipitation over steep topography [START_REF] Argüeso | Precipitation over urban areas in the western Maritime Continent using a convection-permitting model[END_REF]2020], misrepresent the amplitude of the diurnal cycle [START_REF] Hassim | The diurnal cycle of rainfall over New Guinea in convection-permitting WRF simulations[END_REF][START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF] or introduce errors in the rainfall modulation by the MJO phase [START_REF] Vincent | A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent[END_REF][START_REF] Wei | Diurnal Cycle of Precipitation Over the Maritime Continent Under Modulation of MJO: Perspectives from Cloud-Permitting Scale Simulations[END_REF]. The challenges posed to models by convective processes in the MC have attracted much attention in the recent years because of their central role in the climate at multiple scales. A perfect example of this interest is the international initiative "Years of the Maritime Continent" (YMC Phase 1 2017-2020, Yoneyama and Zhang [2020]), aimed at coordinating international modelling and observational efforts to advance our understanding of the MC weather-climate systems, and to improve the representation of convective processes and precipitation in models.

Precipitation in the Maritime Continent tends to concentrate over the islands [START_REF] Qian | Why Precipitation Is Mostly Concentrated over Islands in the Maritime Continent[END_REF],

where rainfall is characterized by a strong diurnal cycle that most models struggle to capture. As described by [START_REF] Ruppert | Island Rainfall Enhancement in the Maritime Continent[END_REF], the "island rainfall enhancement" effect and the land precipitation diurnal cycle are ultimately linked to differences between land and ocean in surface heat capacity and surface energy fluxes. They also show that the diurnal cycle of solar radiation governs mesoscale circulations (i.e., land-sea and mountain-valley breezes), which in turn fuel the convective development, help organize deep convection into mesoscale convective systems and recharge the convective instability required for intense rainfall rates. Local steep topography further contributes to organize convection by exciting and coupling with gravity waves and, in certain situations, to reinforce it by inducing orographic lifting. These convective systems then propagate offshore, assisted by gravity waves, reversed breezes (land breeze) and cold pools [START_REF] Ruppert | Diurnal forcing and phase locking of gravity waves in the maritime continent[END_REF][START_REF] Yang | Climate Variability over the Maritime Continent and Its Role in Global Climate Variation: A Review[END_REF], which together produce a diurnal cycle over water that peaks between night and early morning, albeit generally weaker than over land. Therefore, rainfall features in the MC strongly depend on local factors such as land-ocean contrasts, mesoscale circulations, moisture convergence, intense convective instability, and topography.

In the context of climate change, we expect horizontal and vertical warming contrasts that may induce changes in the intensity and spatial distribution of precipitation. For example, differences in warming rates between continents and ocean can alter mesoscale circulations [START_REF] Joshi | Mechanisms for the land/sea warming contrast exhibited by simulations of climate change[END_REF] and vertical warming contrasts can modify atmospheric stability [START_REF] Wang | Understanding Future Change of Global Monsoons Projected by CMIP6 Models[END_REF], which will likely affect rainfall in the Maritime Continent. At the ocean basin scale, thermal contrasts will also play a key role in defining future changes of the Walker Circulation [START_REF] Yim | Land-sea thermal contrast determines the trend of Walker circulation simulated in atmospheric general circulation models[END_REF] -and hence in the El Niño-Southern Oscillation-, which ascending branch is anchored to the Maritime Continent and likely modulated by the "island rainfall enhancement" effect [START_REF] Ruppert | Island Rainfall Enhancement in the Maritime Continent[END_REF]. At continental and seasonal scales, monsoons will be also intensified due to enhanced thermal contrasts [START_REF] Seth | Monsoon Responses to Climate Changes-Connecting Past, Present and Future[END_REF], which may redistribute precipitation.

In the Maritime Continent, [START_REF] Lambert | Land-Ocean Shifts in Tropical Precipitation Linked to Surface Temperature and Humidity Change[END_REF] did not find a consistent shift of rainfall from ocean to land due to warming, as opposed to other tropical regions (i.e., Amazonia). Bony et al.

[2013] also identified land-sea thermal contrasts as having an important role in modifying tropical rainfall patterns over land, although they deemed the dominant factor in tropical overturning circulation to be the higher CO2 concentrations and the resulting radiative imbalance in the atmosphere. However, all these changes were established using Global Climate Models (GCMs), which have serious difficulties in representing crucial features of the Maritime Continent rainfall [START_REF] Jourdain | The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations[END_REF][START_REF] Schiemann | The sensitivity of the tropical circulation and Maritime Continent precipitation to climate model resolution[END_REF][START_REF] Baranowski | Contemporary GCM Fidelity in Representing the Diurnal Cycle of Precipitation Over the Maritime Continent[END_REF][START_REF] Yang | Climate Variability over the Maritime Continent and Its Role in Global Climate Variation: A Review[END_REF]. Recent studies have generated future climate projections using RCMs [START_REF] Supari | Multi-model projections of precipitation extremes in Southeast Asia based on CORDEX-Southeast Asia simulations[END_REF][START_REF] Tangang | Projected future changes in rainfall in Southeast Asia based on CORDEX-SEA multi-model simulations[END_REF] and found that both increases and decreases in land precipitation over areas of the MC were plausible, depending on the region and the season examined. Yet, the physical mechanisms driving such changes have not been explored. Therefore, the use of higher-resolution models to better understand the mechanisms driving future changes in precipitation over the region is still necessary. Despite some errors that persist in high-resolution models, experiments that explicitly resolve convection are especially beneficial in coastal areas, regions of complex topography and locations with frequent and intense deep convection [START_REF] Prein | A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges[END_REF][START_REF] Lucas-Picher | Convection-permitting modelling with regional climate models: latest developments and next steps[END_REF]], all of which apply to the Maritime Continent. In this study, we use a model at convection-permitting scales to investigate the response of rainfall to a warming climate. For the first time, we examine future climate information at convection-permitting scales in the entire Maritime Continent and determine the different response of rainfall extremes to climate change in parameterized and explicit convection experiments. We also conducted a novel analysis of the role of warming spatial contrasts, land-sea breeze circulations and modified atmospheric stability in modulating this response. We expect that landsea thermal contrast changes will affect breeze circulations, and vertical differences in the response to global warming will modify vertical profiles and stability. In combination, they will have implications for future climate precipitation regimes in the region. Since the interactions between convection and the environment are represented differently in explicit and parameterized convection models, precipitation responses to climate change will likely differ between these models too. In view of this possibility and the expectations around convection-permitting models for future projections [START_REF] Fosser | Convection-Permitting Models Offer Promise of More Certain Extreme Rainfall Projections[END_REF][START_REF] Prein | Special issue: Advances in convection-permitting climate modeling[END_REF][START_REF] Lucas-Picher | Convection-permitting modelling with regional climate models: latest developments and next steps[END_REF], we explore the rainfall response to increased temperature in both explicit and parameterized convection experiments at very high resolution.

The rate at which precipitation responds to surface temperature changes is known as precipitation scaling [START_REF] Trenberth | Conceptual framework for changes of extremes of the hydrological cycle with climate change[END_REF][START_REF] Held | Robust responses of the hydrological cycle to global warming[END_REF] and has two main components: dynamical and thermodynamical (see Box 11.1 in Seneviratne et al. [2021]). In the past, also the co-variational term was explored [Bony et al. 2004]. The thermodynamical component is linked the Clausius-Clapeyron relationship -increased atmospheric water-holding capacity with temperature-and is a primary mechanism for climate-scale precipitation changes. Under climate change conditions, this should contribute to more intense precipitation rates, particularly for extremes [Dobrinski et al. 2018]. The dynamical component includes changes in large-scale circulation patterns that determine the supply of moisture and in local circulations that contribute to vertical motions. The intensification of sea-breeze, atmospheric instability and convective processes is framed in the latter group (i.e., local circulations). Here, we focus on the role of local circulations to explain the spatial patterns of precipitation changes, which is related to the dynamical part. However, we also explore the contribution of thermodynamical changes to provide a comprehensive picture. We examine the separate contribution of thermodynamical and dynamical mechanisms to intense precipitation changes in the region and we identify differences between parameterized and explicit convection models in this context.

Methods

Model and present-climate experiments

In this study, we use the Weather Research and Forecasting (WRF) Model v3.9.1 [START_REF] Skamarock | A Description of the Advanced Research WRF Version[END_REF] ; 2013-2014, 2014-2015 and 2015-2016), each preceded by a 10-day spin-up period (22 nd October -31 st October) that was discarded in the analysis. The analysis period covers the wet season for most of the Maritime Continent and the selected years span both positive and negative phases of the El Niño/Southern Oscillation.

The physical parameterization schemes are among the most widely used options and were chosen according to previous studies over the region [START_REF] Li | Resolution dependence of the simulated precipitation and diurnal cycle over the Maritime Continent[END_REF][START_REF] Argüeso | Precipitation over urban areas in the western Maritime Continent using a convection-permitting model[END_REF][START_REF] Vincent | A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent[END_REF]. The setup consists in the WRF single moment 6-class microphysics scheme [WSM6; [START_REF] Hong | The WRF single-moment 6-class microphysics scheme (WSM6)[END_REF], the Yonsei University (YSU) scheme for Planetary Boundary Layer turbulence, the Rapid Radiative Transfer Model scheme for longwave radiation (RRTM), the Goddard scheme for shortwave radiation, the Noah land surface model and the MM5 similarity scheme for the surface layer. In the parameterized convection experiment, the Betts-Miller-Janjic [BMJ; Betts andMiller, 1986, 1993;Janjic, 1994] scheme was chosen to represent both deep and shallow convection, while it was turned off for the explicit convection experiment (see [START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF] for additional details on the model configuration). In this framework, clouds are driven by the microphysics parameterization regardless of the convective representation. However, the environmental conditions in the model will depend on the convective representation and thus the microphysics scheme will likely generate different clouds. In the explicit simulations, it is the model equations (dynamical core) that represent convection and its effects on the atmosphere vertical profile. In the parameterized runs, the BMJ scheme does not directly inhibit explicit convection, but it reduces the potential for explicit convection because it removes energy from the atmosphere through the adjustment towards a stable vertical profile. Explicit convection at scales resolved by the model grid is still possible though, especially in environments that favor strong convective processes.

Present climate experiments are directly initialized and driven by ERA5 reanalysis [START_REF] Hersbach | The ERA5 global reanalysis[END_REF]] at circa 0.3° spatial resolution and updated every 6 hours. [START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF] already analyzed these simulations and compared them against satellite-derived products to examine the role of different convective representations and the spatial resolution in representing realistic rainfall features. They concluded that, as opposed to higher resolution experiments, 4-km provides the best estimates of precipitation while maintaining computational and storage costs affordable. This is the upper boundary of the convective grey zone [START_REF] Prein | A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges[END_REF], a range of spatial resolutions (~4-10km) where explicit and parameterized convection may compete. It is yet unclear whether convection should be parameterized in this range. Thus, it further justifies the analysis of both parameterized and explicit convection setups.

Pseudo Global Warming experiments

This study expands the existing set of runs in [START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF] by incorporating Pseudo Global Warming (PGW, [START_REF] Schär | Surrogate climate-change scenarios for regional climate models[END_REF]) experiments to examine the response of rainfall to a particular climate change signal. These experiments were built adding the climate change signal obtained from a global climate multi-model ensemble from the Coupled Inter-comparison Model Project Phase 5 (CMIP5). We calculated the seasonal cycle of all variables ingested by the model (i.e., wind components, humidity, geopotential height and temperature at pressure levels; and 2-m dew-point temperature, 2-m temperature, 10-m wind components, surface pressure, mean sea level pressure, and sea surface temperature) for historical and future (2080-2100) climate experiments from 33 GCMs (Supplementary material). We computed the climate change signal for each calendar month, each variable, and each model, and interpolate them to a common 0.72° grid to calculate a multi-model mean climate change signal. These monthly changes are linearly interpolated in time to 6-hourly intervals and nearest-neighbor interpolated to the ERA5 grid. Then they are added to ERA5 to create the initial and boundary conditions to drive WRF under a synthetic future climate scenario. The PGW method has been previously evaluated in a "perfect model approach" with satisfactory results [START_REF] Yoshikane | Verification of the Performance of the Pseudo-Global-Warming Method for Future Climate Changes during June in East Asia[END_REF][START_REF] Donat-Magnin | Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet[END_REF] and was applied in a wide range of studies from tropical cyclones [START_REF] Chen | Impacts of climate change on tropical cyclones and induced storm surges in the pearl River Delta region using pseudo-global-warming method[END_REF] to ice sheets [START_REF] Donat-Magnin | Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet[END_REF].

The future climate change signal is here represented by the Representative Concentration Pathway 8.5 (RCP8.5 scenario). We chose this high-emission scenario to examine the impact of a marked climate change signal on the Maritime Continent, but this choice does not imply any assumptions on its likeliness.

Even though this experiment set up does not constitute a rigorous future climate projection and thus has limitations, it offers numerous advantages. While runs are not long enough to be completely representative of the climate, their duration is a very good reason to force future climate runs with climatological anomalies from the GCMs multi-model ensemble (MMM). This way, our results are less dependent on the interannual variability produced by individual models for specific years. Furthermore, it is often argued that changes projected by the MMM are in general more credible than projections produced by individual models [START_REF] Knutti | Challenges in Combining Projections from Multiple Climate Models[END_REF]. In addition, CMIP5 models' biases are stationary even under strong climate changes [START_REF] Krinner | Striking stationarity of large-scale climate model bias patterns under strong climate change[END_REF], hence this method partly overcomes the biases in the climate states produced by individual models.

The MMM estimates are still subjected to biases, such as the Sea Surface Temperature warming patterns that require complex bias-correction methods [START_REF] Dutheil | Impact of projected sea surface temperature biases on tropical cyclones projections in the South Pacific[END_REF], but the same applies to direct downscaling of individual highly biased GCMs. The PGW method includes the thermodynamical effects produced by a given climate change signal and some dynamical features.

For example, it considers the mean change in the large-scale dynamics because the geostrophic balance is linear. However, it misses changes in the structure and variability of circulation patterns, as well as some non-linear large-scale dynamics and variability. These include phenomena such as MJO or ENSO, some of which may undergo frequency changes in the future [START_REF] Cai | Increased variability of eastern Pacific El Niño under greenhouse warming[END_REF].

Another potential limitation is that non-stationary biases that may be shared by CMIP5 models are not removed with the multi-model mean. It is important to note that these features can affect the spatial patterns of precipitation changes in our experiments.

Overall, the method constitutes an interesting, efficient, and solid approach to quantify the response of the climate change system to plausible future conditions. It also brings the analysis down to the physical-process level at feasible computational costs, especially considering the demands of convection-permitting experiments at continental scales. In this study, we refer to the PGW simulations as "future" experiments for clarity reasons. Changes projected by the selected multi-model ensemble over the Maritime Continent are shown in the supplementary material (Supplementary Figure S1) for temperature, wind, precipitation, and integrated water vapor.

Scaling calculation

We first calculate the scaling of extreme precipitation with temperature directly from the model outputs. To further understand the underlying mechanisms, we also calculate changes in precipitation extremes using the physical scaling diagnostic described in O' Gorman and Scheneider [2009b], which estimates precipitation rates from vertical profiles of vertical pressure velocity, temperature, and pressure. This methodology is usually applied to high-percentile precipitation events by selecting times when they occur. It expresses the precipitation rate during an extreme event at each grid point as:

𝑃 𝑒 ~-{𝜔 𝑒 𝑑𝑞 𝑠 𝑑𝑝 | 𝜃 * ,𝑇 𝑒 } Eq. 1
Where Pe is the precipitation amount during an extreme event, ωe is the corresponding vertical pressure velocity, {•} is a mass-weighted integral over the troposphere, and the remainder is the vertical derivative of the saturation specific humidity qs at constant saturation equivalent potential temperature θ* (i.e., moist adiabatic) and evaluated at the mean temperature Te during the intense rainfall event (See O' Gorman and Scheneider [2009b] for additional details). By comparing estimates from present-and future-climate experiments, it is possible to approximate the full precipitation scaling for extremes, which aggregates the effects of thermodynamic and dynamic processes.

Following [START_REF] Pfahl | Understanding the regional pattern of projected future changes in extreme precipitation[END_REF], we can decompose changes in heavy rainfall into thermodynamic and dynamic contributions. To calculate the separate effect of thermodynamic processes, we ignore changes in the vertical profile of ωe and we use instead the time-average from present climate experiments calculated only for extreme precipitation hours (𝜔 𝑒_𝑝𝑟𝑒𝑠𝑒𝑛𝑡 ̅̅̅̅̅̅̅̅̅̅̅̅̅). The new heavy rainfall estimates (Pe_thermo, Eq. 2) will be due to changes in the vertical derivative of qs only. The dynamic contribution (Pe_dyn, Eq.3) is calculated by subtracting the thermodynamic scaling (Eq 2) from the full scaling (Eq. 1).

𝑃 𝑒_𝑡ℎ𝑒𝑟𝑚𝑜 ~-{𝜔 𝑒_𝑝𝑟𝑒𝑠𝑒𝑛𝑡 ̅̅̅̅̅̅̅̅̅̅̅̅̅ 𝑑𝑞 𝑠 𝑑𝑝 | 𝜃 * ,𝑇 𝑒 } Eq. 2 𝑃 𝑒_𝑑𝑦𝑛 = 𝑃 𝑒 -𝑃 𝑒_𝑡ℎ𝑒𝑟𝑚𝑜 Eq. 3
In this study, we apply for the first time this diagnostic to convection-permitting experiments and hourly model outputs to disentangle the contribution of thermodynamic and dynamic processes and explain possible deviations from the Clausius-Clapeyron relationship at very high spatiotemporal scales. Prior to applying the scaling, we horizontally smoothed the vertical pressure velocity (ωe) -not the vertical derivative of qs -using a gaussian filter with a standard deviation of 20 km. Reasons for this include: 1) to reduce the effect of downdrafts produced by intense rainfall within the convective cell and characterize the environment producing the extreme episode, 2) to filter out the influence of single-cell storms that models may generate at these scales [Murata et al., 2017a] and 3) to better match the scales previously used with this method (~100 km) [START_REF] Pfahl | Understanding the regional pattern of projected future changes in extreme precipitation[END_REF]. Scaling was also calculated without any prior spatial smoothing.

Results

Precipitation changes in explicit and parameterized convection models.

In this section, we analyze changes in mean precipitation as simulated by the parameterized (PA) and explicit (EX) convection experiments. We compare present and future experiments over three consecutive austral summers (NDJF) and calculate changes relative to present-climate values ([future-present]*100/present). Mean precipitation changes show a prominent contrast between a net increase over land and a decrease over the ocean (Fig. 2). Both model configurations simulate a domain-average decrease in mean precipitation (-13.3% for PA and -6.2% for EX), probably because they are driven by the same boundary conditions, which exert control over the large-scale dynamics. The MMM of the CMIP5 ensemble used here to create the PGW scenario project a slight increase in the domain-average rainfall (4.9%), but precipitation is a model prognostic variable and is not used to generate the boundary conditions. While this difference in the sign of changes may seem contradictory, the link between the large-scale conditions and precipitation in models is complex. Factors that contribute to this disparity include differences in the spatial scale (grid-averaging effects), in the efficiency of convective schemes, in the response of convective schemes to environmental changes and in surface evapotranspiration. Furthermore, the CMIP5 ensemble projects a wide range of possible changes over the domain (between -16.6% and 19.8%).

Despite these differences and the divergence across CMIP5 models [START_REF] Ipcc | Annex I: Atlas of Global and Regional Climate Projections Supplementary Material RCP8[END_REF]and Supplementary Figure S1], the ensemble consistently projects larger increases over land than over the ocean, which is coherent with our results (Fig. 2).

Both our experiments produce an increase in rainfall over land (4.3% PA; 5.0% EX) and a decrease over the ocean (-20.2% PA; -12.7% EX). Narrow waters in between islands and coastal areas are notable exceptions to this general response. Thus, according to our experiments, a warmer atmosphere would generate more rainfall over land in the Maritime Continent, although the spatial variability is large over both land and ocean (Fig. 2e). Changes are similar across all three austral summers in the PA runs and results from one year deviate from the other two in the EX run (Fig. 2e and Supplementary Figure S2). Altogether, this provides confidence on the robustness of the results.

The spatial pattern of precipitation changes is broadly similar in both experiments, but there are features in the response that differ between the two. Firstly, the magnitude of changes: PA produces larger decreases of rainfall over the ocean. Secondly, EX experiments are spatially noisier and so is the response to warming. This is partly explained by the length of the simulations, but PA is spatially much smoother, which suggests that the noise is inherent to how convective processes are represented. In fact, changes in EX shows a larger year-to-year variability than PA under the same large-scale conditions (Fig. 2e). This contrast is relevant because the next generation of climate projections will shift towards convection-permitting models [START_REF] Prein | A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges[END_REF][START_REF] Prein | Special issue: Advances in convection-permitting climate modeling[END_REF], which may produce less spatially homogeneous projections if they behave like our model. None of the two model projections can be deemed more likely than the other because their relative performance varies depending on the metric analyzed [START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF]. However, explicit convection brings increased realism of precipitation features often misrepresented in models such as the diurnal cycle and its coupling with the land-sea breeze [START_REF] Birch | Sea-Breeze Dynamics and Convection Initiation: The Influence of Convective Parameterization in Weather and Climate Model Biases[END_REF][START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF].

In addition to mean precipitation, we analyze the model extreme precipitation response to a climate change signal and quantify the role of convective representation in that response (Fig. 3).

We characterize changes in precipitation events through a range of percentiles (50 th to 99.9 th ) and focus on the upper tail of the distribution (95 th , 98 th , 99 th and 99.9 th percentiles). The statistical significance of changes was tested at the 90% confidence level using a bootstrapping approach based on resampling with replacement following [START_REF] Contractor | Intensification of the Daily Wet Day Rainall Distribution Across Australia[END_REF][START_REF] Contractor | Changes in Observed Daily Precipitation over Global Land Areas since 1950[END_REF]. For each grid point, we concatenated present and future, and resampled the resulting timeseries with replacement under the null hypothesis that there is no change. The same permutation is used for all grid points to preserve spatial dependence in the resampled data. The resampling was done using 12-hour blocks to preserve temporal dependence of events. This assumes independence of rainfall from one 12-hour period to the next, which is supported by the distinct diurnal cycle of rainfall in the region. The diurnal cycle was considered when defining the blocks (06-18UTC and 18-06UTC) so that the rainfall peak at around 5pm LST is not split across two blocks. This approach neglects correlation in rainfall on daily or longer timescales, which could lead to some overestimation in the number of independent samples.

We split the resampled timeseries in two equal parts and calculate the change in the percentiles between the two. The process was repeated 1000 times to build a distribution of quantile change ratios between the two, which is normally distributed around 0, and estimate the p-values of the original quantile change ratios. Percentiles were calculated for each grid point and each period (present and future) separately. Percentiles can be calculated using all hourly values [START_REF] Schär | Percentile indices for assessing changes in heavy precipitation events[END_REF] or wet-only values [START_REF] Chan | Downturn in scaling of UK extreme rainfall with temperature for future hottest days[END_REF] depending on the purpose of the analyses. While both may have advantages, we chose the all-hourly values approach because having a fix population of events ensures that: a) changes in each percentile univocally means that events exceeding that percentile (extreme) must change, b) upper percentiles are not affected by changes in light rain frequency, and c) percentiles represent a fixed number of events at all locations, runs and periods.

For instance, the upper percentiles we focus on represent circa 433, 173, 87, and 9 hourly events in all grid cells and all experiments. These advantages make our results easier to interpret in the context of this study. Present climate values of these percentiles are provided in the supplementary material (Supplementary Figure S3). Changes in percentiles using wet-only values (>0.1 mm hr -1 ) are shown in Supplementary Figure S4 for comparison with the all-hourly values approach.

Changes in wet-only percentiles are comparable to higher percentile changes using all-hourly values. This is because wet-only percentiles characterize a higher section of the rainfall distribution tail. Although changes in wet-only percentiles are affected by changes in the entire distribution of rainfall, the spatial patterns between the two approaches are very similar for very intense precipitation events. They both show a land-sea contrast. Differences between PA and EX are also similar using one approach or another.

Domain-average changes of precipitation percentiles are summarized in Table 1 along with separate changes over land and ocean. These changes were computed using the mean of percentiles each region (domain, land, and ocean) for both present and future runs, and then the relative changes with respect to present climate were calculated. PA suggests a decrease in all selected percentiles when aggregating over the entire domain and ocean grid points. It also suggests an increase in all but the 95 th percentile over land. EX projects a similar behavior, except for the highest one (99.9 th ), which increases both over land and ocean. On average, EX produces larger decreases for the 95 th and 98 th percentiles compared to PA. This difference is strongly dominated by decreases over water in EX, since changes over land are very similar between the two experiments. As we move to more intense precipitation events (99 th ), the contrast between land and ocean is further enhanced, especially for PA, which suggests substantial changes both over land and ocean but with opposite signs (9.6% vs. -32.6% respectively). Indeed, some large islands such as New Guinea show a strong and statistically significant response of the upper tail to warming in the PA experiment (Fig. 3i).

The highest end of the distribution (99.9 th percentile) represents events above 10 mm hr -1 in most cases and well above 25 mm hr -1 in many land grid points (Supplementary Figure S3). In EX, the 99.9 th increases over land (14.9%) under climate change conditions, but there is no clear signal over the ocean. In fact, significant changes are mostly located over land. PA shows an increase in extreme precipitation (99.9 th ) over land (22.4%) too, but it produces significant decreases over the ocean (-10.9%). As a result, PA exhibits a domain-average decrease of high-end extremes (-3.2%), while EX produces an increase (4.0%).

Figure 3 provides a more detailed and visual description of the precipitation response to warming. As opposed to the summary discussed above (Table 1), Figure 3 includes aggregated information on other percentiles (50 th to 100 th ) and shows the spatial variability of changes. Rates below the 90 th percentiles are usually light rainfall events (<0.1 mm hr-1) in EX (Supplementary Figure S3), and their contribution to total precipitation is limited. The contribution of events below the 90 th percentile to total precipitation may be larger in PA, because the 0.1 mm hr -1 rate is reached at a much lower percentile (70 th ). This is likely due to the drizzle effect that typically affect models with convective schemes [START_REF] Gutowski | Temporalspatial scales of observed and simulated precipitation in central U.S. climate[END_REF][START_REF] Sun | How often does it rain?[END_REF][START_REF] Dai | Precipitation characteristics in eighteen coupled climate models[END_REF][START_REF] Stephens | Dreary state of precipitation in global models[END_REF][START_REF] Pendergrass | Two modes of change of the distribution of rain[END_REF].

Moderate rainfall events (95 th percentile) decrease with warming in most locations according to both experiments (Fig. 3c-d). Very few grid-points show an increase of moderate precipitation and they are mostly located over or near the islands. The land-sea contrast becomes increasingly clear in the upper percentiles for both experiments (Fig. 3c-j). However, EX already concentrates rainfall over land much more than PA under present climate conditions, thus the contrast will become even sharper in EX under warming. Therefore, the spatial pattern of mean precipitation changes (Fig. 2) is largely explained by changes in the high-end of the distribution, according to

Thermodynamical and dynamical contributions to precipitation changes.

Our experiments suggest that the islands of the Maritime Continent will undergo higher precipitation rates and more intense rainfall extremes in a warmer climate. Mechanisms that produce rainfall changes are often interlaced and their contributions may act in opposite directions.

We quantified their combined effect by calculating changes in high percentiles of precipitation and express them as a ratio with respect to the domain-average near-surface warming (Direct method). Using near-surface temperature to estimate rainfall scaling imposes important limitations because it does not consider changes in moisture availability, which often play a primary role in defining precipitation extremes [Lenderink et al., 2017]. Also, it assumes homogeneous warming in the vertical and thus does not allow for different changes in the atmospheric water-holding capacity at different vertical levels. However, it is a standard way of measuring the precipitation scaling [Westra et al., 2014;Bao et al., 2017;Lenderink et al., 2017;Dobrinski et al., 2019;Allan et al., 2020] because it relies on widely available observations and model outputs. We also estimated the aggregated contribution of the thermodynamical and dynamical terms of rainfall scaling with temperature using the theoretical approach described in section 2.3 [O'Gorman and Schneider, 2009a], and we refer to this method as Full scaling. The precipitation scaling was decomposed into thermodynamical and dynamical terms following [START_REF] Pfahl | Understanding the regional pattern of projected future changes in extreme precipitation[END_REF]. The direct method serves as a backdrop to test the adequacy of the theoretical approach (full scaling).

We focus on the scaling of the 99 th percentile. For all methods we quantified the scaling by calculating the mean of all events above the percentile for each period and each grid-point, and compute the change relative to present climate values, as we have done with other precipitation changes. Then, we divide it by the domain-average near-surface temperature change and estimate the scaling of intense rainfall with temperature for both experiments (Fig. 4 a-d). The direct scaling calculation shows a strong land-sea contrast of the scaling in PA. Most islands undergo increases in the range 10-20% K -1 , while much of the ocean experience decreases, sometimes as large as -20% K -1 . The spatial pattern of scaling is similar in EX, but the rates are not as pronounced.

The full scaling estimates (Fig. 4 c-d) yields results that compare very well with the direct scaling calculation, including both the pattern and the magnitude of changes. Figure 4 a-d supports the idea that warming leads to processes favoring more intense precipitation over land, while changes of any sign are plausible over water. Ignoring changes in vertical velocities in Eq. 1 (see details in section 2.3), we can estimate the separate contribution of thermodynamic (Thermo)

processes to the scaling (Fig. 4 e-f). The thermodynamic contribution to extreme precipitation changes is spatially much more homogeneous than the full scaling, although it still presents some land-sea contrast in EX. In both experiments, the thermodynamic processes contribute to increases in the range 4.2-8.2% K -1 over virtually the entire domain (95% of grid-points), with slightly higher values for EX (4.3-8.2% K -1 ) than for PA (4.2-7.5% K -1 ). This is roughly consistent with the Clausius-Clapeyron relationship, which establishes an approximate increase in rainfall rates of 7% per degree of near-surface warming [Trenberth et al., 2003]. The scaling methods produce similar results when using different percentiles (95 th and 99.9 th , see Supplementary Figures S5 andS6, respectively).

In the results above, the vertical pressure velocity (ωe) was smoothed using a 20-km gaussian filter prior to calculating the scaling. We have also calculated the scaling without the smoothing to determine the impact of this choice (Supplementary Figure S7) and found that the smoothing considerably reduces the noise in both model configurations, particularly in EX. While the overall pattern is similar between the two approaches, the spatial details and magnitude of the full (theoretical) scaling is closer to the direct scaling when smoothing ωe.

Large spatial variations in direct and full scaling estimates can only be explained by dynamic processes, because the thermo term is spatially homogeneous. The contribution of dynamic processes is calculated as the difference between the full scaling and the thermo term [START_REF] Pfahl | Understanding the regional pattern of projected future changes in extreme precipitation[END_REF]. The dynamical term enhances or opposes the effect of the thermodynamic mechanisms through changes in vertical motions during extreme precipitation events. Therefore, heterogeneities in vertical motions are responsible for the spatial noise in the dynamical term (Supplementary Figure S8), which modulates the homogeneous thermodynamic contribution. This explains the existence of positive and negative values of direct and full scaling close together.

Although this may be alleviated with longer runs and strengthening the gaussian spatial filter applied to the vertical velocity, differences between the two experiments suggest that the nature of the convective scheme may also play a role in smoothing out spatial heterogeneities of vertical motions. In fact, explicit convection experiments running at few-kilometer resolutions are prone to generate single-grid-cell precipitating systems [Murata et al., 2017a] that may reflect into this spatial noise.

In general, our results indicate that the dynamic term counteracts the thermodynamic effect over the ocean. Over land, dynamical processes tend to enhance precipitation scaling in PA. In EX, the dynamic contribution also presents a land-sea contrast, but both positive and negative contributions were obtained over land. This land-sea contrast is consistent with results in [START_REF] Pfahl | Understanding the regional pattern of projected future changes in extreme precipitation[END_REF] using GCMs, which suggested that dynamic processes enhanced changes in daily precipitation extremes over large islands in the Maritime Continent. Differences between our two model configurations are further discussed in the next section, where the vertical structure of the atmosphere is analyzed.

Land-sea thermal contrasts, stability, and precipitation changes.

Our hypothesis is that warming contrasts play a key role in the spatial pattern of the archipelago's rainfall response to a changing climate. Changes in moisture availability due to increased atmospheric water-holding capacity and changes in the large-scale dynamics are spatially too uniform to explain the fine spatial structure of precipitation changes. In the horizontal, land warms faster than the ocean due to their different heat capacity, which intensifies current land-sea thermal contrasts. In the vertical, changes in temperature and humidity profiles may be different at each location, which affects atmospheric stability at different rates. These spatial contrasts create more favorable conditions for mesoscale circulations and increased potential for convective initiation. Whether this potential is realized depends on the convective representation.

For example, future climate change increases the land-sea thermal contrast (Fig. 5a-b) and the domain-average increase is very similar in both experiments. Air over land warms faster (3.30°C in both cases) than the air above the ocean (2.90°C in EX; 2.94°C in PA). Changes in thermal contrast help explain precipitation changes to some extent, as we described in the previous section, but differences between parameterized and explicit runs indicate that the convective representation is crucial to define the rainfall response to this thermal forcing.

Here, we investigate the mechanism that links changes in thermal land-sea contrast and rainfall.

The primary source of convective potential over the islands is the moisture flux convergence at the lower levels. Moisture flux convergence was calculated using:

𝑀𝐹𝐶 = -∇ • (𝑞𝑽 ℎ ) Eq. 4
Where MFC is the moisture flux convergence, q is water vapor mixing ratio at 2m and Vh is the horizontal wind vector at 10 m. The approach described by Bluestein [1992] was applied to deal with discrete variables.

Because of the enhanced land-sea thermal contrast, the model produces an increase in nearsurface MFC along the coastlines (Fig. 5 c-d). This agrees with results in [START_REF] Tangang | Projected future changes in rainfall in Southeast Asia based on CORDEX-SEA multi-model simulations[END_REF],

which also found an increase in low-level moisture flux convergence over the islands using multiple regional climate simulations. This MFC increase on the coastline is accompanied by a decrease far outside over the ocean, where negative values are observed almost everywhere. MFC was decomposed into two terms (horizontal advection of specific humidity and horizontal mass convergence; Eq. 5) following [START_REF] Banacos | The use of moisture flux convergence in forecasting convective initiation: Historical and operational perspectives[END_REF]:

𝑀𝐹𝐶 = -𝑽 ℎ • ∇𝑞 -𝑞∇ • 𝑽 ℎ Eq. 5
and then changes were calculated for each of them to estimate their relative contribution to MFC changes. This decomposition reveals that MFC changes where largely driven by horizontal convergence changes, while advection changes play a negligible role on average (Fig. 5 e-h).

Therefore, a higher MFC -along the coast and mostly driven by convergence changes-points in the same direction as our hypothesis that mesoscale circulations (sea breeze type) intensify under warmer conditions. This effect is even more marked during the time of the day when seabreeze usually builds up in the region (10-16 LST, Supplementary Figure S9). Hence, our results are coherent with the idea that land-sea thermal contrasts and the resulting MFC changes are drivers of rainfall redistribution and more intense precipitation over land.

This mechanism may be partly responsible for the intensification of rainfall over land, but there must be other factors (e.g., stability) causing differences in precipitation changes between the two experiments despite their very similar changes in thermal contrasts and MFC.

Under global warming, the upper troposphere warms faster than the lower troposphere in the tropics, which increases dry static stability [START_REF] Schneider | Water vapor and the dynamics of climate changes[END_REF][START_REF] Chou | Increase in the range between wet and dry season precipitation[END_REF]. We estimated dry static stability in the lower troposphere from both our experiments using the difference in potential temperature (θ) between the lower (850 hPa) and the middle (500 hPa)

troposphere. In both simulations, this difference is reduced in the future, thus indicating increased dry static stability under climate change, especially for the explicit convection run (Supplementary Figure S10). However, these changes in temperature are also accompanied by changes in humidity, which directly affect moist adiabatic processes that govern deep convection. To incorporate this factor, we analyzed changes in potential stability (also called moist static stability or convective stability). Herein, we speak in terms of instability to make the interpretation of results more intuitive, but it is conceptually the same. We examined the equivalent potential temperature (θe) and its vertical profiles, which accounts for changes in both temperature and humidity. The difference in θe between 900-800 hPa and 600-400 hPa layers provides a measure of potential instability.

This choice is motivated by the fact that atmospheric models (e.g., CMIP5 ensemble, Supplementary Figure S11) often show a discontinuity in the vertical derivative of θe, which is likely linked to how convective processes are parameterized. The discontinuity is linked to how the schemes work around the freezing level, their interaction with microphysics schemes and the quasi-equilibrium profile used in certain parameterizations such as the Betts-Miller Janjic, which is used in the parameterized simulations here. Indeed, this behavior is also detected in PA around the 500 hPa level. This reflects on changes of the vertical profiles simply because the discontinuity is shifted upwards. To reduce the dependence of our results on this issue, which we assume is a model artifact, we computed potential instability using the above reference layers.

Vertical profiles of equivalent potential temperature (θe) reveal that time-mean potential instability will increase everywhere under the prescribed climate change signal according to both experiments (Fig. 6c). This is shown by steeper vertical profiles of θe under future climate conditions with the largest increases in θe near the surface (~14-15 K). Changes over land are on average very similar between PA and EX, and both show an intensification of instability in the early afternoon (Fig. 6d). On the other hand, changes over the ocean are slightly larger in PA and are flat throughout the day in both model runs. This spatial distribution of potential instability changes is further illustrated in Figure 6 e-f, which suggest that instability will increase the most over large land masses. These changes were tested for statistical significance using a Mann-Whitney U test at the 99% confidence level and they are significant everywhere in the domain.

The land-sea contrast of potential instability changes is more pronounced in EX, mostly because EX produces more moderate changes over water. If we select only days when precipitation exceeds the 99.9 th percentile in each grid cell and calculate the changes in potential instability, EX produces much stronger changes than PA during such events (Supplementary Figure S12). Therefore, despite similar time-mean changes in potential stability and mean precipitation changes, EX suggest more intense extreme precipitation in a warming climate accompanied by higher potential instability.

To understand the link between this increase in potential instability and precipitation extremes, we focus on potential instability preceding events above the 99.9 th percentile. To that purpose, we selected 0.5° by 0.5° areas in the four largest islands (squares in Fig. 1) and calculated the areaaveraged potential instability (θe 850hPa -θe 500hPa ) over the 12 hours before any grid cell exceeds its 99.9 th percentile of hourly precipitation. Figure 7 shows the relationship between the intensity of heavy rainfall events versus the preceding potential instability for present and future simulations, both model configurations and over the four selected representative areas. Retaining only dry hours to calculate convective instability as opposed to all preceding hours was tested with no substantial differences (not shown) and we decided to keep the all-hour approach so that all instability values were calculated using the same number of preceding hours. Different accumulation periods (6, 18, 24 hours) and area sizes (0.2°, 1.0°) were also tested with very similar results (not shown).

Different locations within each island were also examined to ensure our results were robust and the outputs were qualitatively the same (Supplementary Figures S14 andS15).

In all cases, the atmosphere reaches a clearly different state where both potential instability and extreme rainfall intensity are higher under a warmer climate. While potential instability alone is not enough to determine the intensity of extreme events for each of the periods separately, this is not surprising since many other factors are involved in the generation of heavy rainfall events, thus the dispersion of the point clouds. However, it indicates that the model response to climate change in terms of precipitation extremes and potential instability are related to each other.

This relationship also reveals an interesting contrast between model runs. The fully explicit convection is much more dispersed in the rainfall-instability space depicted in Fig. 7. Some of the differences between experiments noted before can be interpreted through this feature. For instance, it shows that the convective parameterization restricts the atmospheric conditions to a given range because it continuously adjusts the vertical profile towards an equilibrium state. The explicit run, on the contrary, is more flexible in this sense, and it allows for higher precipitation rates (Supplementary Figure S14 andS15). In fact, it also produces situations with substantially larger potential instability when the extremes occur (Fig. 7). As a result, the response to climate change in PA is spatially more homogeneous than in EX, which produces a noisier signal because of the higher degrees of freedom explicit convection provides. Precipitation rates above the 99.9 th percentile were also compared to other variables such as potential temperature (θ), Convective

Available Potential Energy (CAPE) and precipitable water (PW) to illustrate their links in a changing climate (Supplementary Figure S16). In agreement with our previous findings, changes in the intensity of extreme rainfall are related to an increase in dry static stability (θ 850hPa -θ 500hPa ) and an increase in latent conditional instability (CAPE). It also shows that this contrast between changes in dry static stability (temperature dependent) and convective stability (temperature and moisture dependent) when extremes occur is mostly due to a higher availability of precipitable water, which significantly increases in the future.

Present and future bivariate distributions of rainfall and the various instability metrics (Fig. 7 and Supplementary Figures S14-S16) were tested statistically to determine if they are significantly different. All present and future bivariate distributions were statistically different to each other at the 0.01 significance level using a multi-dimensional version of the Kolgomorov-Smirnov (KS)

test [Fassano and Franceschini, 1987]. A classical 1-D KS test was also applied to present and future precipitation distributions and were found different at the 0.01 significance level too.

Increased instability only produces precipitation changes if convective circulation is intensified.

Thus, changes in vertical motions must be considered to explain the spatial contrasts of rainfall changes (see section 3.2). Here, we relate changes in vertical pressure velocity (ωe) that precede precipitation extremes with changes in the extremes themselves (Fig. 8). We binned grid points by changes in extreme rainfall (total accumulated above the 99 th percentile). For each bin, we computed the average change in the vertical profile of ωe over the six hours preceding each extreme event. We chose a six-hour period because vertical motions due to convection start approximately six hours before the peak of the precipitation diurnal cycle in the region [START_REF] Argüeso | Precipitation Features of the Maritime Continent in Parameterized and Explicit Convection Models[END_REF].

On average, the atmospheric environment preceding intense rainfall is characterized by ascending motions almost through the entire troposphere in both experiments and climate periods (Supplementary Figures S17 andS18). Only the bottom and the top levels show small positive values (descending motions). Thus, positive changes ωe can be generally interpreted as a weakening of ascending vertical motions. Both experiments concentrate stronger vertical motions and rainfall extremes over land, which is consistent with the picture described in section 3.2. The average ωe preceding rainfall extremes and its changes are spatially more homogeneous in the parameterized case (not shown), which is also consistent with the results above (Fig. 7 and Supplementary Figures S14-S16).

Not only rainfall extremes are collocated with more intense upward motions, but also their changes. Areas where rainfall extremes will increase the most coincide with stronger upward motions, particularly above 800hPa (red in Fig. 8). Likewise, extreme rainfall decreases are accompanied by weakened ascending winds (blue in Fig. 8). Weakened vertical motions (blue) extend across the zero-change line, hence small increases (<20%) in extreme precipitation occur with decreases in vertical rising motions, especially over land and when convection is explicit (Fig. 8f). It is likely that changes in ωe partly offset the effect of warming, but the latter still dominates in this range. These results are consistent with the decomposition of scaling in dynamical and thermodynamical terms, where vertical motions help explain spatial contrasts in extreme rainfall changes. Similar results were obtained for other percentiles too (95 th and 99.9 th ; not shown).

Most ocean areas show a decrease in precipitation extremes (99 th percentile; Fig. 3g-h and Fig 8c-d) and weakening of vertical motions preceding such intense rainfall events (Fig. 8c-d). On the other hand, upward vertical velocity before extreme events tend to intensify where heavy rainfall increases over the ocean.

This aggregated view reveals some similarities and differences between the two convective representations. Both runs expand the range of possible extremes to higher values, especially EX (Supplementary Figures S17-S18

Conclusions

We studied the role of horizontal and vertical warming contrasts on precipitation changes in the Maritime Continent for the late 21 st century under a RCP8.5 scenario using a Pseudo-Global Warming approach. We analyzed results from a regional climate model operating at convectionpermitting scales with two different representations of deep convection: parameterized and explicit.

We found that the model produces a domain-averaged decrease of rainfall during the Maritime Continent wet season (NDJF) for both convective representations, although there is a marked landocean contrast. Both model configurations tend to produce a decrease over the ocean and an increase over land. Even though GCMs do not agree on the sign of rainfall changes for the region, their spatial pattern of changes is consistent with the land-ocean contrast we obtained [START_REF] Jourdain | The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations[END_REF][START_REF] Wang | Understanding Future Change of Global Monsoons Projected by CMIP6 Models[END_REF]. The ensemble mean of GCMs selected in this study projects a domain-average increase in rainfall and in the vertically integrated water vapor. Thus, the decline of domain-average precipitation suggested by our model experiments cannot be explained by changes in the large-scale water vapor supply (i.e., advection). Instead, it must be explained by processes that transform the available water vapor into precipitation and how they are represented in models.

Our experiments suggest that the islands of the Maritime Continent will undergo more intense mean and extreme precipitation in a warmer climate. However, extremes behave differently under the same large-scale climate change signal depending on how convection is represented. This includes their magnitude, spatial pattern, and the relative changes of the various percentiles. The most prominent difference is that the land-sea contrast of changes is more pronounced in the parameterized runs. The upper percentiles of rainfall undergo larger increases relative to present climate extremes when convection is parameterized. However, explicit convection expands the range of possible future extremes to higher values. This is partly because present climate extremes in EX are already more intense than in PA, but also because the convective scheme constrains the response to warming. Therefore, future generations of climate projections at convection-permitting resolutions may project different outlooks for rainfall extremes to those currently available.

We determined the contribution of thermodynamical and dynamical processes to changes in rainfall extremes under a warmer climate. Thermodynamic effects account for changes in precipitation extremes that are consistent with the Clausius-Clapeyron relationship, and their contribution is relatively homogeneous across the domain. Thus, we need to invoke dynamical processes to explain features of extreme rainfall changes, such as their magnitude range (-20% K - 1 to 20% K -1 ), their spatial contrasts and the divergences between the two model runs.

According to our simulations, the primary driver of changes in the spatial distribution of rainfall Deep convection entails intense upward vertical motions. Thus, extreme precipitation is linked to high vertical pressure velocity. We found that the model tends to concentrate strong vertical motions and rainfall extremes over land, especially when convection is explicitly resolved. In some land areas, this is enhanced in future climate simulations, which explains departures from the thermodynamic contribution to extreme rainfall changes. Changes in vertical winds also indicate a possible expansion of the convective cell over the islands and a slight weakening of upward motions in the mid troposphere. Even though explicit convection produces more extreme rainfall events over land in the future -due to further concentration of upward motions over large islands and the lack of convective scheme constrains-, the parameterized case suggests stronger changes relative to present climate values. In fact, the model produces stronger changes in vertical motions preceding extreme events with climate change when convection is parameterized. This refers not only to the strengthening of vertical winds over land, but also to their weakening over the ocean.

In both model configurations, areas of stronger upward vertical pressure velocity are collocated with positive changes in extreme precipitation, and vice versa. This spatial coincidence, together with the scaling decomposition into thermodynamical and dynamical terms, evidences the role of vertical motions in modulating the intensity of future climate rainfall events.

In summary, we showed that the way convection is represented is crucial in defining the model response to warming, because it defines dynamical processes that shape the future distribution of precipitation and the intensity of extremes. 98 th (e, f), 99 th (g, h) and 99.9 th (i, j) percentiles for the parameterized (left column) and explicit (right column) model setups. Statistical significance was tested using a bootstrap approach based on 12-hour blocks resampling with replacement repeated 1000 times. According to the test, 43.2%, 46.7%, 42.1% and 19.5% of all the grid-points show significant changes for the corresponding percentiles in the parameterized runs, and 52,2%, 35.3%, 25.8% and 13.1% for the explicit case.

Non-significant changes were masked out.

  to simulate the atmosphere over the Maritime Continent and investigate rainfall patterns under climate change conditions. The model was run at 4-km spatial resolution over a domain covering 5916 km by 2556 km (1479 by 639 grid points) in the Maritime Continent and resolving the vertical with 50 hybrid coordinate levels. The experiments span three consecutive austral summers (1 st November to 1 st March

Figure 3 .

 3 Figure 3.

  scheme may help explain these differences in vertical motions and precipitation intensities.

  is the land-sea thermal contrast and its enhancement under climate change. Land warms faster than the ocean, which favors local sea-breeze type circulations. These circulations increase low-level moisture flux convergence over land and contribute to create conditions for deep convection development over land. They are also responsible for suppressing rainfall generation over the ocean to some extent.Deep convection and heavy rainfall require atmospheric instability to occur. Climate change modifies the vertical profile of the atmosphere and thus alters the overall stability. Although dry static stability increases under future climate conditions because the upper half of the troposphere warms faster, the combined effect of temperature and humidity changes in the vertical makes the atmosphere more unstable in terms of moist static stability. While both model experiments show this response to climate change, the convective scheme constrains potential instability and extreme precipitation values within a narrower range. Under future climate conditions, this means the model with explicit convection allows heavier rainfall events to occur. Yet, changes relative to present climate values are higher in the parameterized case. Also, the convective scheme produces a response to climate change that is spatially more uniform, while explicit convection generates noisier patterns of extreme precipitation changes.

Figure 1 :

 1 Figure 1: The Maritime Continent archipelago. The region shown corresponds to the model

Figure 2 :

 2 Figure 2: Present mean precipitation rates in the parameterized (a) and the explicit convection

Figure 3 :

 3 Figure 3: Changes in rainfall percentiles. Statistics of changes over the entire domain (gray),

Figure 4 :

 4 Figure 4: Scaling of the 99 th percentile of precipitation with respect to the domain-average 2-m

Figure 6 :

 6 Figure 6: Atmospheric potential instability changes in parameterized and explicit convection runs.

Figure 7 :

 7 Figure 7: Extreme precipitation events (>99.9 th percentile) versus convective instability in

Figure 8 :

 8 Figure 8: Changes in vertical pressure velocity (ωe) versus changes in mean precipitation rates of events above the 99 th percentile of hourly rainfall averaged over all (a, b), ocean-only (c, d) and land-only (e, f) grid points for the parameterized (left column) and the explicit (right column) convection experiments. Each panel includes a probability distribution of extreme precipitation changes at the top to indicate the most frequent values of changes in the domain.

Table 1 :

 1 Domain-average changes in mean precipitation and the upper percentiles of hourly rainfall for the parameterized (PA) and explicit (EX) convection experiments. Changes for all grid points, land-only grid points and ocean-only grid points. Changes are in percentage with respect to present climate and positive changes are in bold typeface.

			Mean	95 th	98 th	99 th	99.9 th
		Total	-13.3	-9.8	-14.4	-19.2	-3.2
	PA	Ocean	-20.2	-11.8	-22.0	-32.6	-10.9
		Land	4.3	-5.3	1.5	9.6	22.4
		Total	-6.2	-18.9	-17.3	-12.8	4.0
	EX	Ocean	-12.7	-47.1	-33.4	-23.7	0.6
		Land	5.0	-5.3	0.6	5.4	14.9

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Acknowledgements

This study was supported by REHIPRE and COASTEPS projects. REHIPRE is funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions Individual Fellowship Grant Agreement 743547 (H2020-MSCA-IF-

Data availability statement 34

The data on which this paper is based is too large (~ 120TB) to be publicly archived with available resources. The model version used in this study is open source and publicly available (https://github.com/NCAR/WRFV3/releases). Sample namelists to reproduce the experiments are archived and available in a public repository (10.5281/zenodo.4624353). All retained data is available upon request addressed to Daniel Argüeso (d.argueso@uib.es).

2016-743547). The COASTEPS project (CGL2017-82868-R MEIC/AEI/EU FEDER) is financed by the Spanish Ministry of Economy, Industry and Competitiveness, and partially funded by the EU FEDER funds. D. Argüeso was also funded by Spanish Ministry of Science and Innovation through the Research Project EPICC project (PID2019-105253RJ-I00) and the Beatriz Galindo Programme (BG20/00078). We thankfully acknowledge the computer resources at MareNostrum 4 and the technical support provided by the Barcelona Supercomputing Center (AECT-2018-3-0002 and AECT-2019-1-0002). We also thank NCAR and other participating institutions for making the WRF-ARW Model publicly available. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank each of the climate modelling groups for producing and making available their model output. for Climate Extremes (Australia), D. Argüeso and A. Di Luca would also like to thank their scientific and technical support. We thank Steve Sherwood for his insights on changes in equivalent potential temperature. We would like to thank two anonymous reviewers whose comments helped improve and clarify this manuscript.

according to a Mann-Whitney U test at the 99% confidence level are masked out in (b) and (d).

These areas are 7.9% and 14.7% of the domain, respectively. Boxplots show changes for all (gray), ocean-only (blue) and land-only (red) grid cells for parameterized (hatched) and explicit (solid) experiments. The boxes represent the interquartile range, whiskers are the 10 th -90 th percentile range, horizontal lines are the median changes over all grid points in each category, and markers show the median for each of the 4-month periods individually.