Mégane Missaire
email: megane.missaire@gmail.com

Nicolas Fraize

Jean-Christophe Comte

Bruno Truchet

Régis Parmentier

Paul-Antoine Salin

Gaël Malleret
email: gaelmalleret@gmail.com

Working and Reference Memory tasks trigger opposed long-term synaptic changes in the rat dentate gyrus

Keywords: Working Memory, Reference Memory, Synaptic Plasticity, Dentate Gyrus, Radial maze

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Main Text

Introduction

Encoding and retrieving memories are fundamental processes for animal survival, especially when it comes to remembering the position of food sources in an environment. Such spatial memory processes mainly rely on the dorsal hippocampus, and have been suggested to depend on selective changes in synaptic transmission between related neurons [START_REF] Takeuchi | The synaptic plasticity and memory hypothesis: encoding, storage and persistence[END_REF]. Many studies linked such synaptic changes with memory processes using indirect, molecular or structural approaches [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Hayashi-Takagi | Labelling and optical erasure of synaptic memory traces in the motor cortex[END_REF][START_REF] Matsuo | Spine-type-specific recruitment of newly synthesized AMPA receptors with learning[END_REF][START_REF] Ryan | Engram cells retain memory under retrograde amnesia[END_REF]. However, a comprehensive, direct and unbiased account for the synaptic modifications occurring in real time in the brain after memory tasks is lacking. Moreover, while several types of memory have been described, it remains unclear whether they are supported by different synaptic plasticity processes.

In the taxonomy of memory, a major dichotomy has been established between long-term memory and short-term/working memory (WM). Long-term synaptic changes have mainly been studied and associated with long-term memory processes, based on the assumption that such processes should leave in the brain a durable trace (also called engram) [START_REF] Tonegawa | Memory Engram Cells Have Come of Age[END_REF].

It is commonly assumed that long-term memory relies on a sustained increase in synaptic transmission (similar to the one observed after an artificial induction of long-term potentiation or LTP [START_REF] Bliss | Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path[END_REF])), and numerous attempts have been made to detect such synaptic changes in the brain after learning [START_REF] Clarke | Plastic modifications induced by object recognition memory processing[END_REF][START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Gruart | Involvement of the CA3-CA1 synapse in the acquisition of associative learning in behaving mice[END_REF][START_REF] Pavlowsky | Persistent modifications of hippocampal synaptic function during remote spatial memory[END_REF][START_REF] Takeuchi | The synaptic plasticity and memory hypothesis: encoding, storage and persistence[END_REF][START_REF] Whitlock | Learning induces long-term potentiation in the hippocampus[END_REF]. The most direct method to detect such changes is certainly to record in vivo, in freely moving animals, evoked responses (field excitatory post-synaptic potentials, fEPSPs) at synapses of interest after training these animals in tasks involving long-term memory processes. However, the detection of these LTP-like changes after learning remains sparse and highly variable in amplitude [START_REF] Gruart | A Differential and Timed Contribution of Identified Hippocampal Synapses to Associative Learning in Mice[END_REF][START_REF] Whitlock | Learning induces long-term potentiation in the hippocampus[END_REF]. Such differences could be explained by the time period during which fEPSPs were recorded. Indeed, fEPSPs were either recorded during the few minutes of the learning sessions [START_REF] Gruart | Involvement of the CA3-CA1 synapse in the acquisition of associative learning in behaving mice[END_REF], or after memory tasks but only up to 4h [START_REF] Whitlock | Learning induces long-term potentiation in the hippocampus[END_REF] or 6h [START_REF] Clarke | Plastic modifications induced by object recognition memory processing[END_REF] post-training. However, none of these studies recorded synaptic changes continuously beyond this time period. This lack of "long-term" studies is surprising as critical reorganizations related to memory consolidation have long been observed during specific time windows occurring up to 24h after learning [START_REF] Smith | Sleep states, memory processes and synaptic plasticity[END_REF].

Contrary to long-term memory, short-term or working memory (WM) is believed to rely not on long-term synaptic changes, but on the persistent activity of neurons encoding items stored during short delays of retention [START_REF] Fuster | Neuron activity related to short-term memory[END_REF][START_REF] Goldman-Rakic | Cellular basis of working memory[END_REF][START_REF] Miyashita | Neuronal correlate of visual associative long-term memory in the primate temporal cortex[END_REF]. However, this sustainable increase in firing rates may not be metabolically efficient and is not always observed during the whole retention delay [START_REF] Rainer | Timecourse of object-related neural activity in the primate prefrontal cortex during a short-term memory task[END_REF][START_REF] Shafi | Variability in neuronal activity in primate cortex during working memory tasks[END_REF]. Therefore, some authors recently proposed a synaptic theory of WM [START_REF] Mi | Synaptic Correlates of Working Memory Capacity[END_REF][START_REF] Mongillo | Synaptic theory of working memory[END_REF], relying on a form of short-term presynaptic plasticity. However, no theory has ever suggested that long-term synaptic changes involving post-synaptic modifications could be associated with some aspects of WM. Indeed, WM is believed to be a short-term form of memory that once retrieved is supposed to be forgotten [START_REF] Dudchenko | An overview of the tasks used to test working memory in rodents[END_REF], or transferred into long-term memory. Nevertheless, it is well known that WM is sensitive to proactive interference, such as past and irrelevant information previously stored in WM that can interfere with the recall of newer and more useful information [START_REF] Roberts | Remembrance of places lasts: Proactive inhibition and patterns of choice in rat spatial memory[END_REF][START_REF] Underwood | Interference and forgetting[END_REF][START_REF] Wixted | The psychology and neuroscience of forgetting[END_REF].

Erasingor inhibiting the recall of proactive interference would therefore be essential for WM function [START_REF] Dudchenko | An overview of the tasks used to test working memory in rodents[END_REF]. Such process could rely on long-term synaptic changes, especially those related to long-term synaptic depression [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Nicholls | Transgenic mice lacking NMDAR-dependent LTD exhibit deficits in behavioral flexibility[END_REF]. However, until now, no study has ever followed the dynamics of long-term synaptic changes after WM training.

To palliate this lack of knowledge, we conducted a long-term electrophysiological study in freely-moving rats using an innovative comparative behavioral approach. Rats were trained in a radial maze in either a long-term -also called "reference"memory (RM) task, or one of two WM tasks: a Low Interference (LIWM) or a High Interference WM task (HIWM) involving variable levels of proactive interference that differentially alter their level of performance [START_REF] Fraize | Levels of Interference in Long and Short-Term Memory Differentially Modulate Non-REM and REM Sleep[END_REF][START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF][START_REF] Missaire | Long-term effects of interference on short-term memory performance in the rat[END_REF]. Having previously shown that the dentate gyrus (DG) of the dorsal hippocampus [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF] could play a major role in processing these interference, we performed long-term recordings of fEPSPs at the perforant path (PP) to DG synapse during two consecutive days. Our results show that while RM training promotes a delayed (>9h) increase in synaptic transmission that is positively correlated with next day performance of the trained rats, WM training also triggers long-term synaptic changes. We show that efficient processing of proactive interference in WM is correlated to a synaptic depression that is observed immediately after LIWM training and may support adaptive forgetting. In contrast, an inefficient processing of these interference during HIWM training depends on a delayed synaptic potentiation.

Results

A comparative approach to study reference memory, working memory and interference processing

The three behavioral tasks used in this experiment were performed in a 8-arm radial maze and involve the use of spatial, allocentric based [START_REF] Bontempi | Time-dependent reorganization of brain circuitry underlying long-term memory storage[END_REF][START_REF] Maviel | Sites of neocortical reorganization critical for remote spatial memory[END_REF][START_REF] Poirier | Qualitatively different hippocampal subfield engagement emerges with mastery of a spatial memory task by rats[END_REF] and hippocampus-dependent [START_REF] Eichenbaum | The hippocampus, memory, and place cells: is it spatial memory or a memory space?[END_REF]O"Keefe, 1993) memory. These tasks were designed in order to elicit the same level of motivation, locomotion of habituation in all trained animals. For instance, we controlled that the number of rewards collected and number of runs in the maze were equivalent for all three groups of rats and at any given session. Such controls allowed us to be certain that the difference observed at the synaptic level (evoked fEPSPs) between groups were directly linked to the cognitive process involved in each task (consolidation of long-term memory in RM versus processing of interference in WM).

Two of these tasks involved WM and were based on the same delayed-non-match-toplace (DNMTP) paradigm (see Materials and Methods) (Fig. 1A). However, the LIWM (Low Interference Working Memory) task was less repetitive than the HIWM (High Interference Working Memory) task. The pair of arms used in the LIWM task was thus different for each of the four trials of a given session. In contrast, during HIWM training, this pair of arms was always the same for all trials and sessions. It has consistently been found that the negative impact of proactive interference on WM increases with the level of similarity between items previously stored into WM [START_REF] Cohen | Effects of varying trial distribution, intra-and extramaze cues, and amount of reward on proactive interference in the radial maze[END_REF][START_REF] Loess | Short-term memory and item similarity[END_REF][START_REF] Underwood | Interference and forgetting[END_REF]. This is why the level of interference is considered higher in the HIWM task in which the same pair of arms is repeatedly presented to the rat. The third task used in this study was a classical RM (Reference Memory) task during which rats had to learn the fixed position of two food rewards in two arms of the maze (Fig. 1A).

Chronically implanted rats (see Materials and Methods and next paragraph) were trained in these three tasks for 10 sessions per day on two consecutive days (Fig. 1A). Behavioral performance in these three tasks is expressed as the percentage of correct choices for blocks of two sessions, as displayed in Fig. 2A. For both WM tasks, performance was high (~70% of correct choices) at the very beginning of the task, probably because of the innate tendency of the rats to exhibit spontaneous alternation, a behavior that naturally causes rodents to choose a different option (visit arm #2) than the one previously adopted (visit arm #1), and in consequence to alternate exploration between two open arms [START_REF] Tolman | Purpose and cognition: the determiners of animal learning[END_REF]. For LIWM rats, performance increased across sessions on Day 1 (ANOVA 1: F 3.351,43.56 = 7.406, p = 0.0003 ***), on Day 2 (ANOVA 1: F 2.183,28.38 = 5.253, p = 0.0098 **), and also between Day 1 and Day 2 (Fig. 2B). On the contrary, for HIWM rats, no performance increase was observed during Day 1(ANOVA 1: F 2.910,40.75 = 1.631, p = 0.1983), Day 2 (ANOVA 1: F 3.136,43.90 = 1.615, p = 0.1980), or between Day 1 and Day 2 (Fig. 2B). Consequently, the performance of HIWM rats was significantly lower than the performance of LIWM rats from the last block (B5) of Day 1 (p = 0.0235) and during Day 2.

Such difference in the level of performance of rats trained in LIWM and HIWM tasks has systematically been observed in previous studies [START_REF] Fraize | Levels of Interference in Long and Short-Term Memory Differentially Modulate Non-REM and REM Sleep[END_REF][START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF][START_REF] Missaire | Long-term effects of interference on short-term memory performance in the rat[END_REF] and likely reflects a non-optimal processing/forgetting of interference in rats training in the HIWM task. This inefficient processing of interference is further revealed by a between-session analysis of performance computed by trials instead of days (Fig. 2C). Such analysis shows that a difference in performance is observed between the two groups after a 24hperiod of rest on the very first trials completed on Day 2 [START_REF] Missaire | Long-term effects of interference on short-term memory performance in the rat[END_REF]. This result suggests that 24h-old information may impact the recall of recent (Day 2) information.

In contrast, performance of rats trained in the RM task started at a lower level than the one observed in WM groups, below 40% and close to the chance level calculated at 34% for this RM task [START_REF] Fraize | Levels of Interference in Long and Short-Term Memory Differentially Modulate Non-REM and REM Sleep[END_REF]. This task is indeed less "intuitive" for the rats and goes against their innate spontaneous alternation preferencesee above. However, their performance rapidly increased during Day 1 (ANOVA 1: F 2.800,44.80 = 27.14, p < 0.0001 ****), Day 2 (ANOVA 1: F 2.633,42.13 = 12.82, p < 0.0001 ****), and between Day 1 and Day 2 (Fig. 2B).This increase in performance allowed RM rats to display on Day 2 a performance level equivalent to the one observed in LIWM rats (~100% at the end of Day 2) but higher than the one displayed by HIWM rats. To sum up, one group of rats succeeded in performing a WM task (LIWM), another failed to do so (HIWM), and a third group succeeded in learning a RM task.

We therefore used these behavioral groups to study and compare the long-term synaptic changes triggered by WM and RM tasks. We chose to record synaptic responses at the Perforant Path (PP) to Dentate Gyrus (DG) synapse, given the involvement of the DG in spatial long-term memory [START_REF] Nanry | Colchicine-induced alterations of reference memory in rats: role of spatial versus non-spatial task components[END_REF][START_REF] Xavier | Dentate gyrus-selective colchicine lesion and disruption of performance in spatial tasks: difficulties in "place strategy" because of a lack of flexibility in the use of environmental cues?[END_REF], spatial WM [START_REF] Niewoehner | Impaired spatial working memory but spared spatial reference memory following functional loss of NMDA receptors in the dentate gyrus[END_REF][START_REF] Sasaki | Dentate network activity is necessary for spatial working memory by supporting CA3 sharp-wave ripple generation and prospective firing of CA3 neurons[END_REF][START_REF] Xavier | Dentate gyrus and spatial behaviour[END_REF], and more specifically in the processing of proactive interference in spatial WM [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF]. Rats were therefore chronically implanted with a stimulating electrode in the PP and a recording array of 8 LFP electrodes in the DG (see Materials and methods). fEPSPs were continuously recorded before (24h Baseline period) and after (for 21h) training (that lasted 3h) on Day 1 (Fig. 1B).

Opposed long-term synaptic changes after successful WM or RM training

Synaptic responses at the PP-DG synapse were obtained at the 8 electrodes implanted to target the DG for each rat. However, among these 8 electrodes, only recordings fulfilling precisely defined histological and electrophysiological criteria (see Materials and methods SI Appendix) were kept for analysis (corresponding to 6.7±1.4 standard deviation electrodes per rat in average). As shown in Fig. 3, the fEPSPs were relatively stable over the two days of recordings, indicating that the electrodes did not move despite the behavioral task.

The slope of the PP-DG fEPSPs recorded in the three behavioral groups was averaged by 6-hour periods during Baseline and Day 1 (post-training) (Fig. 3A). The repeated-measure ANOVA 2did not reveal any significant effect of Group nor of the interaction Group x Time on Baseline or Day 1. However, a progressive decrease of the fEPSP slope could be observed in the three groups on Baseline. This decrease was not due to a progressive impairment of the synaptic responses, but to a circadian modulation of the synaptic responses at the PP-DG synapse (Fig. S1) that was previously described [START_REF] Barnes | Circadian rhythm of synaptic excitability in rat and monkey central nervous system[END_REF]. In order to take into account this circadian effect, we performed direct comparisons between the fEPSP slope recorded on Baseline and the one observed on Day 1 at the same time period, and we did so separately for the three behavioral groups (Fig. 3B). We thus observed an early and significant synaptic depression of 5% in the LIWM group on Day 1 (compared to Baseline), and more specifically in the first 3 hours following training (ZT3-6) (p < 0.0001). On the contrary, in the HIWM group, a significant synaptic potentiation of 3-4% was observed on Day 1 (compared to Baseline) much later on, between 3 and 15 hours post-training (ZT6-12: p = 0.0407, ZT12-18: p = 0.0142).

Similarly, in the RM group, we observed a late synaptic potentiation on Day 1 (compared to Baseline), with an amplitude of 4%, occurring between 9 and 21h hours post-training (ZT12-18: p = 0.0198, ZT18-24: p = 0.0450). This analysis thus revealed opposed long-term modifications of the synaptic transmission at the PP-DG synapse after training in two WM tasks involving different levels of proactive interference (synaptic depression after LIWM trainingsynaptic potentiation after HIWM training). We can also note the opposed synaptic changes after successfully performing a WM task (LIWMsynaptic depression) or a RM task (synaptic potentiation).

These synaptic changes were monitored on average on all the reliable electrodes for a given rat. Interestingly, we also found that modifications of the synaptic transmission could be locally observed. Indeed, the standard deviation of the recorded fEPSP slope between-electrodes and within-animal was found to be significantly increased on Day 1 (compared to Baseline) at all time periods and in the three groups, except in the LIWM group for the first 3 hours immediately following training. Interestingly, this period was precisely the one during which we detected the synaptic depression in this group of rats (Fig. 3C,D). This result suggests that while training in general induces modifications at synapses more specifically engaged in the engram of a given memory (leaving other synapses unmodified), the synaptic depression after successful WM training (LIWM) is a more generalized process that can be recorded at multiple sites of recordings (at least in the DG).

The long-term synaptic changes detected after WM or RM training are correlated with behavioral performance

We then assessed if the opposed long-term modifications of the synaptic transmission that we observed at the PP-DG synapse could be correlated with the behavioral performance of the rats. We first asked whether behavioral performance at the end of training on Day 1 could have an impact on the synaptic changes observed after training. We thus correlated the behavioral performance of the three groups of rats observed on Block 5 (last 2 sessions of Day 1) with the fEPSP slope in the following four 6-hour periods of Day 1 (normalized to 24h of Baseline fEPSP) (Fig. 4A). With each dot representing one animal on the illustrative scatter plot on the right, we could calculate Spearman's rho, which was then plotted for each time period and behavioral group on the main graph on Fig. 4A (left). A significant negative correlation (r = -0.334, p = 0.0186) was found between performance at B5 and fEPSP slope during the first 3 hours (ZT3-6) post-training in the LIWM group. In other words, LIWM rats with good performance at B5 display a more important synaptic depression during the first 3 hours post-training. For the HIWM group, we also observed a significant negative correlation (r = -0.573, p = 0.0264) between performance at B5 and fEPSP slope, but on later time period, at ZT12-18 on Day 1. As we showed that during this timeframe, HIWM rats display on average a synaptic potentiation (Fig. 3B), this late synaptic potentiation could be associated with bad performance on B5.

We also asked whether synaptic changes occurring after training on Day 1 predict behavioral performance on Day 2.First, we correlated the fEPSP slope in the four 6-hour periods of Day 1 (normalized to 24h of Baseline fEPSP) with behavioral performance at Block 6 (two first sessions of Day 2) for the three behavioral groups (Fig. 4B). For the HIWM group, a significant negative correlation was found between the fEPSP slope at ZT12-18 of Day 1 (r = -0.672, p = 0.0128) and performance at B6. Note that the correlation tends to extend to ZT18-24 (r = -0.5382, p = 0.0562). Therefore, the synaptic potentiation we observed at ZT12-18 for the HIWM group appears to be correlated with decreased performance on Day 2. On the contrary, for the RM group, even if not significant, a positive correlation between fEPSP slope in the last 6 hours of Day 1 (ZT18-24) and performance at B6 (r = 0.5023, p = 0.0693) can be observed. We thus wanted to know if this possible link between potentiation and superior performance in RM could be stronger at the end of training Day 2, when rats typically reach a plateau and a maximum of performance. We thus performed the same correlations, but this time with average performance during the last 5 sessions of Day 2 (S16-20) (Fig. 4C). With this new analysis, we observed significant positive correlations between fEPSP slope at ZT6-12 (r = 0.5875, p = 0.0301), ZT12-18 (r = 0.5616, p = 0.0396), ZT18-24 (r = 0.5851, p = 0.0309) and performance at S16-20 for the RM group. The synaptic potentiation observed during these time periods on Day 1 (Fig. 3B) could thus be associated with a stronger memory consolidation of the rule to be learned in the task.

Discussion

Our findings show that synaptic changes at the PP-DG synapse can be detected after both spatial RM and WM training. These changes are 1) long-lastingthey are observed for at least 3h and can last more than 6h; 2) often delayed in timethey occur 6 to 15h after training in some cases; and 3) bidirectionalthey can lead to a synaptic depression or synaptic potentiation depending on the cognitive process involved.

Synaptic depression -An adaptive forgetting marker?

We show that WM training can induce long-term changes in synaptic transmission.

Synaptic transmission can thus be decreased or increased whether training (LIWM or HIWM) involved a more or less important processing of proactive interference. More specifically, LIWM training that is associated with successful processing of interference (Fig. 2), is followed by, and correlated with, a synaptic depression (Fig. 3 and Fig. 4A). As we assessed the early slope of fEPSPs, it is unlikely that this synaptic depression could be the result of a long-lasting increase in GABAergic inhibition. On the contrary, this depression would depend on a molecular cascade associated with LTD-like processes as suggested by a recent study from our team [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF]. This western blot showed that LIWM training is associated with a significant decrease in the phosphorylation of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) GluA1 subunits at the Ser-845 site. Ser-845 GluA1 dephosphorylation is considered to be a signal for AMPARs internalization and their lysosomal degradation, that de facto decreases synaptic transmission during LTD [START_REF] Fernández-Monreal | The balance between receptor recycling and trafficking toward lysosomes determines synaptic strength during longterm depression[END_REF]. Since our present in vivo findings show that synaptic depression is immediately observed after LIWM training (ZT3-6), we believe this depression reflects the late phase of an LTD induced in the DG while the rats were still performing the task. This online process (during behavioral training) could reflect adaptive forgetting of prior irrelevant information (pertaining to past trials) and a "reset" of WM content [START_REF] Roberts | Remembrance of places lasts: Proactive inhibition and patterns of choice in rat spatial memory[END_REF]) so that new information may be stored once more in WM during subsequent trials. Rats having the best performance on Day 1 would be those with the most effective adaptive forgetting of WM content, and therefore those displaying the highest LTD (as suggested by our correlation results -Fig. 4A). This interpretation is in agreement with previous work suggesting that LTD would be involved in forgetting processes, and more specifically adaptive forgetting. We previously showed that the genetic inhibition of the Protein Phosphatase 2A (PP2A) that blocks LTD and AMPARs internalization leads to the inhibition of adaptive forgetting of past information in both a flexibilitybased RM water maze task or a WM T-maze task [START_REF] Nicholls | Transgenic mice lacking NMDAR-dependent LTD exhibit deficits in behavioral flexibility[END_REF]. In contrast, inhibiting phosphorylation processes by genetically expressing a dominant negative form of the regulatory subunit of Protein Kinase A (PKA) enhances LTD, flexible learning and the adaptive forgetting of interference in a radial maze WM task similar to the one used in the present study [START_REF] Malleret | Bidirectional regulation of hippocampal long-term synaptic plasticity and its influence on opposing forms of memory[END_REF]. Recently, Malinow and colleagues [START_REF] Nabavi | Engineering a memory with LTD and LTP[END_REF] showed that the optogenetic delivery of an LTD conditioning protocol targeting the auditory input in the amygdala leads to the forgetting of the association between a foot shock and this auditory input previously learned by a rat. Others found that blocking AMPARs internalization prevents normal forgetting of established long-term memories for object location [START_REF] Migues | Blocking Synaptic Removal of GluA2-Containing AMPA Receptors Prevents the Natural Forgetting of Long-Term Memories[END_REF] or the flexible learning of new water maze locations in RM or WM due to an absence of forgetting of previously learned past positions [START_REF] Awasthi | Synaptotagmin-3 drives AMPA receptor endocytosis, depression of synapse strength, and forgetting[END_REF]. Altogether, these results could explain why the efficient processing (forgetting) of interference may prompt LTD-like phenomenon that is observed immediately after LIWM training.

Although prolonged (lasting around 3h), this synaptic depression may be considered of a rather weak in amplitude (a fEPSP slope decrease of 5% compared to baseline). However, even if the group of Manahan-Vaughan studied extensively the artificial induction of LTD in the hippocampus and its modulation after various learning tasks [START_REF] Kemp | Hippocampal long-term depression: master or minion in declarative memory processes?[END_REF], no physiological LTD-like process has ever been clearly reported in vivo after behavioral training. Consequently, it is difficult to compare our findings with others from the scientific literature. Even the artificial induction of homosynaptic LTD at the PP-DG synapse seems difficult to obtain in vivo. Indeed, a heterosynaptic LTD can be easily induced in the medial perforant pathway (MPP) (by inducing LTP on the lateral perforant pathway (LPP)), with an amplitude of 30% of synaptic depression (measured on the slope of the fEPSPs) and lasting at least 3 days [START_REF] Doyère | Heterosynaptic LTD and Depotentiation in the Medial Perforant Path of the Dentate Gyrus in the Freely Moving Rat[END_REF]. However, the direct induction of homosynaptic LTD in one of these pathways leads to a 5 to 10% synaptic depression in some (but not all) animals, lasting only 60 minutes approximately [START_REF] Gonzalez | Low-frequency stimulation induces long-term depression and slow onset long-term potentiation at perforant path-dentate gyrus synapses in vivo[END_REF]. The strength or the present study is that the synaptic depression observed is not induced artificially but physiologically induced, after training in a task that naturally involves adaptive forgetting. As this forgetting process would allow previously stored information (related to past trials) to be put aside or even erased, we can assume that the LTD-like phenomenon we observe corresponds more to a depotentiation rather than a de novo LTD, that would still share with LTD common mechanisms but that is easier to induce than LTD in vivo [START_REF] Bear | Long-term depression in hippocampus[END_REF]. Indeed, some authors have suggested that storage of information in WM would imply synaptic potentiation, and more specifically short-term potentiation (STP - [START_REF] Park | NMDA receptor-dependent long-term potentiation comprises a family of temporally overlapping forms of synaptic plasticity that are induced by different patterns of stimulation[END_REF]). Depotentiation would be the mechanism by which this stored information would be forgotten/erased. Is this synaptic depression a local or widespread mechanism? To test this hypothesis, we computed the average intra-animal and inter-electrode standard deviation to assess differences between electrodes (and therefore synapses) in the DG. We then compared this standard deviation between Baseline and Day 1 by 6h time slots (Fig. 3D). Interestingly, we observe that this variability between recording electrodes is not increased (as it is the case in the other groups or time slots) following LIWM training compared to Baseline in the first 3h period, that corresponds to the time slot during which the synaptic depression is observed. This result suggests that this synaptic depression could thus be generalized to multiple DG synapses rather than being a local phenomenon. One possible interpretation for this result is that repeating numerous trials (40 per day) during LIWM training causes massive storage of information in WM (involving numerous synapses), that would subsequently require massive adaptive forgetting (at the level of the same important number of synapses) to allow deletion (and "reset") of this information in WM, potentially through a generalized and prolonged LTD-like process like the one we observe.

In contrast, the variability between recording electrodes is increased after HIWM training (compared to Baseline) for all time periods of post-training recording (Fig. 3D). This suggests the existence of localized, rather than generalized, modifications in the synaptic transmission in this task. Contrary to what is observed after LIWM training, synaptic transmission is potentiated (by approximately 4%) after HIWM training, and in particular 3 to 15h post-training (Fig. 3B). This potentiation is in agreement with previous results from our team [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF] showing that HIWM training increases GluA1 expression in the DG, as well as the level of phosphorylation of this AMPAR subunit. A massive increase (+ 60%) in the phosphorylation of Ca2+/calmodulindependent protein kinase II (CaMKII) in the DG was also observed in the same study. It has been shown that phosphorylation of CaMKII constitutes an important signal for the induction of LTP, allowing phosphorylation and exocytosis of GluA1-AMPARs at the synaptic level, and de facto the increase/potentiation of synaptic transmission [START_REF] Citri | Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms[END_REF]. As an increase in GluA1 expression is also observed after HIWM training [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF], we can hypothesize that this task promotes the establishment of a late phase of LTP that would require the synthesis of new protein, and in particular new AMPARs. The present in vivo observation of a prolonged (long-term) increase in synaptic transmission after HIWM training seems to confirm the existence of LTP-like phenomena after completion of this task. This synaptic potentiation is also negatively correlated with performance on Day 2 (Fig. 4B), suggesting that this potentiation is deleterious for behavioral performance, and thus for adaptive forgetting. Unlike the synaptic depression observed after LIWM training allowing previously stored information (Day 1) to be put aside or erased, this LTP-like phenomenon after HIWM training could prevent such forgetting process, and contribute to the consolidation of this past information (Day 1) playing the role of interference on the next day (Day 2). This is suggested by the analysis of the behavioral performance of rats trained in the HIWM task. While performance of HIWM rats is lower than the one displayed by LIWM rats only for the last 2 trials (within-session effect) of Day 1, this is the case on Day 2 from the very beginning (first trials) of the training sessions (between-session effect)(Fig. 2C). This result suggests a lack of WM reset for rats trained in the HIWM task: they display impaired performance as soon as training starts on Day 2 because past information (Day 1) has not been put aside/erased from WM (reset) and has even possibly been consolidated in long-term memory [START_REF] Missaire | Long-term effects of interference on short-term memory performance in the rat[END_REF][START_REF] Roberts | Remembrance of places lasts: Proactive inhibition and patterns of choice in rat spatial memory[END_REF]. This consolidation of past information (playing the role of interference on Day 2) would be achieved through a delayed synaptic potentiation (occurring several hours after the end of training) that would have a deleterious effect on the performance of the rats trained in the HIWM task.

Consolidation of information into long-term memory (RM) and delayed LTP.

In contrast to the HIWM group, the synaptic potentiation observed after RM training (Fig. 3B) has a positive effect on the behavioral performance of the rats trained in this task the next day (positive correlation between the synaptic responses recorded on Day 1 and the rats performance in RM on Day 2 -Fig. 4C). Indeed, RM and HIWM training are quite different and even opposite in nature. While the consolidation of past information (Day 1) interferes with the recall of newer information (Day 2) in the HIWM task, this consolidation of past information is required for acquiring the rule (invariant information) during incremental learning of the RM task. This interpretation is in agreement with several in vivo studies showing that training involving long-term memory formation increases synaptic transmission [START_REF] Clarke | Plastic modifications induced by object recognition memory processing[END_REF][START_REF] Gruart | Involvement of the CA3-CA1 synapse in the acquisition of associative learning in behaving mice[END_REF][START_REF] Moser | Potentiation of dentate synapses initiated by exploratory learning in rats: dissociation from brain temperature, motor activity, and arousal[END_REF][START_REF] Whitlock | Learning induces long-term potentiation in the hippocampus[END_REF]. However, most of these studies recorded fEPSPs during the first hours following training, or even for some, only during training itself. The originality of our approach was to continuously record synaptic changes after the first day of RM training. This is how we were able to show that synaptic responses can be durably increased (LTP-like phenomenon) several hours after training (delayed-LTP). If we had only recorded fEPSPs during the first hours post-RM training, we would not have detected any change in synaptic transmission. This could explain why, while there is considerable evidence in the literature that altering molecular mechanisms associated with LTP can prevent memory processes, there is in fact relatively few evidences of LTP-like phenomenon after behavioral training. Consequently, the difficulty to find such alterations in synaptic transmission after training in tasks involving long-term memory could be partly due to the time window analyzed in these studies. This synaptic potentiation delay may seem odd and could lead to reconsider the contribution of DG and/or LTP in RM. However, a lesion of the DG shows that this structure is required in RM [START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF]. Furthermore, as pointed out above, there is a positive correlation this delayed synaptic potentiation and performance at D2. It is known that critical reorganizations of sleep, known to play an important role in memory consolidation [START_REF] Rasch | About sleep's role in memory[END_REF] as well as in synaptic plasticity [START_REF] Diekelmann | SLEEP The memory function of sleep[END_REF][START_REF] Tononi | Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration[END_REF], can take place up to 24 hours after RM training [START_REF] Smith | Sleep states, memory processes and synaptic plasticity[END_REF]. Consequently, the same delayed reorganizations in synaptic transmission could occur after training. Our study brought to light such delayed synaptic modifications following training in a long-term RM task. However, we cannot exclude the fact that an earlier synaptic potentiation takes place but is not recorded at the level of our electrodes, that in fine only correspond to a small portion of the rat"s DG.

In our study, the synaptic potentiation observed after RM training is rather weak, representing an increase in the fEPSP slope of 4% compared to baseline. However, this weak potentiation is certainly much more compatible with physiological processes of memory storage than the massive potentiation (sometimes above 100%) observed in vivo after artificial inductions of LTP. In addition, to detect these synaptic changes, we used extracellular recordings. fEPSPs correspond to the summation of extracellular synaptic currents of multiple synapses localized in the vicinity of the recording electrode (Chaillan et al., 2008). Therefore, large increase in synaptic transmission to individual synapses could be partially balanced at the population level by null or opposed modifications at other synapses. Engram cells studies suggest that only 10 to 30% of the neurons of a given circuit would be recruited for the storage of a given memory. For instance, 20% only of hippocampal CA1 pyramidal cells express the immediate early gene c-Fos after exploration of a new environment [START_REF] Tanaka | The hippocampal engram maps experience but not place[END_REF]. Furthermore, only a fraction of these engram cells" synapses would be modified by memory. Matsuo and colleagues have shown that the increase in synaptic GluA1 recruitment in CA1 after contextual fear conditioning barely affects 3% of dendritic spines [START_REF] Matsuo | Spine-type-specific recruitment of newly synthesized AMPA receptors with learning[END_REF]. Similarly, after training in an inhibitory avoidance task, Bear and colleagues detected synaptic potentiation in CA1 in only 12 out of 44 recording electrodes, other synapses remaining stable or becoming durably depressed [START_REF] Whitlock | Learning induces long-term potentiation in the hippocampus[END_REF].

Our findings are in line with these results. Not only RM training induces a rather weak (but still prolonged) synaptic potentiation (4% compared to baseline), but this potentiation is delayed in time, extremely variable (as indicated by the increase in the variability between recording electrodes -Fig. 3D) and can be reversed by subsequent RM training. Indeed we found that the late synaptic potentiation observed at the end of Day 1 can be replaced by a synaptic depression (compared to Baseline and compared to Day 1) after training Day 2 (Fig. S2). This result is in agreement with previous results from our team showing that overtraining rats in a spaced (1 session per day) RM task induces a decrease (and not an increase as expected) in the expression of LTP markers (phosphorylation of CaMKII) in the DG [START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF]. The synaptic depression we observe after 2 days of RM training (Fig. S2) could participate in the abstraction/semantization of the memory acquired during the RM task. In fact, during RM training, rats would have first to extract and store (through synaptic potentiation?) a general rule of the task by processing invariant information during multiple episodes or trials ("food is located in arms # 1 and # 4 of the maze"). Later on however, they would also have to forget (through synaptic depression?) non-essential information (related for instance to the internal state of the animal during a specific trial) in order 1) not to overflow their brain with irrelevant information and 2) keep a flexible use of the general rule previously acquired. This is also how semantic memory would be generated out of multiple experiences originally processed in episodic memory [START_REF] Hardt | Decay happens: the role of active forgetting in memory[END_REF]. This episodic-to-semantic shift would be very similar to context generalization, the tendency to express a behavior that was once specific to a specific learning context in other contexts. Hardt and colleagues [START_REF] Migues | Blocking Synaptic Removal of GluA2-Containing AMPA Receptors Prevents the Natural Forgetting of Long-Term Memories[END_REF] showed that blocking AMPARs internalization not only prevents normal forgetting of established long-term memories, but also the generalization of contextual fear expression (the natural tendency for an animal to express after several days fear for other contexts than the one originally trained for). Consequently, this result as well as ours suggest that LTD mechanisms could participate to the formation and transformation of RM, from an episodic-based to a semantic-based memory trace.

Materials and Methods

Electrode implantation for chronic recordings. Stereotaxic surgery was performed on 33 Dark Agouti male rats aged 10 weeks. Surgical procedures are further described in detail in SI Appendix. A recording array of 8 LFP electrodes was implanted in the molecular layer of the DG, and a bipolar stimulating electrode was implanted in the PP, coming from the entorhinal cortex. In addition, two EEG electrodes were fixed on the skull (prefrontal and parietal), two EMG electrodes were inserted between the neck muscles, and a reference electrode was fixed above the cerebellum for referential recordings.

Recording setup and electrical stimulations. The detail of the recording setup is described in SI Appendix, and rats remained connected to the setup during the whole duration of an experimental protocol. Electrical stimulations consisted in a monophasic 200µs pulse delivered by an isolated pulse stimulator (model 2100, AM-Systems, U.S.A.). Before all experimental protocols, an input-ouput (I/O) curve was established in order to choose the stimulation intensity used for the whole experiment. This intensity had to elicit an fEPSP with an amplitude corresponding to 75% of the maximal amplitude on the 8 electrodes. During the experimental protocol, stimulations were elicited every 30 seconds, except during the behavioral tasks when stimulations were stopped. Continuous recordings of stable fEPSPs were acquired for 24h before the beginning of the behavioral task and were used as a baseline.

Behavioral tasks and protocol. The three behavioral tasks have been used in previous studies [START_REF] Fraize | Levels of Interference in Long and Short-Term Memory Differentially Modulate Non-REM and REM Sleep[END_REF][START_REF] Fraize | Differential changes in hippocampal CaMKII and GluA1 activity after memory training involving different levels of adaptive forgetting[END_REF][START_REF] Joseph | Differential Involvement of the Dentate Gyrus in Adaptive Forgetting in the Rat[END_REF], and are described in more details in SI Appendix.

Briefly, these three appetitive spatial tasks took place in the same radial maze and were equivalent in terms of duration, locomotion, food rewards, or motivation. The two WM tasks were based on a DNMTP (Delayed Non-Match to Place) paradigm, with 1) a sample phase during which the animal was forced to visit the only open arm of the maze containing a first food reward, and 2) a choice phase during which the animal had to choose between entering the arm previously visited during the sample phase (familiar armwrong choice) that no longer contained any reward, or entering an adjacent new arm (correct choice) containing a second food pellet.

These two phases represented one trial, and were separated by a 15 second-delay (during which rats were placed in an opaque transfer cage on the central platform of the maze), compatible with the storage duration of information (here, the arm visited during the sample phase) in WM. After the end of a given trial, this piece of information stored in WM is no longer useful and needs to be forgotten in order not to interfere proactively with the storage and recall of newer information in WM during the next trials. Indeed, four trials were performed during a given training session. The only difference between the LIWM (Low Interference Working Memory) and HIWM (High Interference Working Memory) tasks concerned the pairs of arms used for each trial in the radial maze during a session. For the LIWM task, different pairs of arms were used for each trial of a given session (4 trials given in 4 different pairs of arms = 8 arms of the radial maze), while for the HIWM task the same pair of arms was used for all trials (equivalent to a Y-maze task). The HIWM task was therefore more repetitive than the LIWM task, with very similar information stored in WM, increasing the level of proactive interference [START_REF] Underwood | Interference and forgetting[END_REF]. The third task used in this study was a RM task. During this task, rats had to learn two fixed positions of food rewards in the maze across 8 trials of a given session (equivalent to the 8 runs -4 trials of 2 phasein the maze performed by HIWM and LIWM rats during a given session).For the three groups, the behavioral protocol took place on two days, with 10 sessions on Day 1 and 10 sessions on Day 2.

For this study, we were interested in the period between these two days of training, during which fEPSPs were continuously recorded, and normalized to the baseline period preceding training. fEPSP slope analysis. The initial slope of the fEPSP waveform was analyzed on the baseline and post-training day 1 periods by performing a linear regression on individual fEPSPs between two cursors (previously defined on an average fEPSP) and by computing the coefficient of determination. This analysis was done for each of the valid electrodes among the 8 LFPs electrodes implanted in the DG for an individual rat (see SI Appendix for electrodes selection criteria), and we averaged the results for all these valid electrodes. is used for all trials and all sessions. For the RM task, a possible sequence of runs chosen by an animal is represented, with the lowered arm on trial n being the one previously visited on trial n-1.

Statistics

In this example, the two rewards were collected on trial 6 and thus all arms were re-opened on trial 7. This task involves long-term storage and consolidation of a fixed rule (reference) in (C) Within-and Between-session effect of proactive interference. Behavioral performance for the LIWM and HIWM groups averaged by two trials (two first or two last trials of a session) and by groups of 5 sessions. During the last 5 sessions of Day 1, performance of HIWM rats was lower than the one displayed by LIWM rats only for the last 2 trials (within-session effect), while on Day 2 this was the case from the very beginning (first trials) of the sessions (between-session effect).

Data are represented as mean ± SEM.

.

  Statistical analyses were performed using PRISM Graphpad Software version 6. Behavioral data (performance scores expressed as a percentage of correct choices), was analyzed using two-way ANOVAs for repeated measures with Block (2 training sessions) and Group (LIWM, HIWM or RM) as main factors. fEPSP slope was analyzed using two-way ANOVAs for repeated measures with Condition (Baseline or Day 1 post-training) and Time period as main factors. Multiple comparisons were performed using Sidak's post hoc test. Correlations were analyzed using Spearman correlation test due to the non-gaussian distribution of at least one of the variable. Data are expressed as means ± s.e.m. Detailed results of the Statistical tests can be found in SI Appendix, and post-hoc results are represented on the graphs using the standard symbols: * : p ≤ 0.05, ** : p ≤ 0.01, *** : p ≤ 0.001, **** : p ≤ 0.0001.

Figure 1 .

 1 Figures and Tables

  memory ("always go to the same two arms to get rewarded'). The lower diagram depicts the training sequence common for the three tasks, with 10 sessions on Day 1 and 10 sessions on Day 2. (B) Recording protocol: fEPSPs at the PP-DG synapse were evoked every 30 seconds for 24h during the Baseline period (starting at ZT0 = 9am). Stimulations were then stopped during the 3 hours of training Day 1, and started again after the behavioral tasks for the remaining 21h (every 30s) before training Day 2 when stimulations were turned off again. The waveform is a classical fEPSP recorded at the PP-DG synapse, from which we computed the slope.

Figure 2 .

 2 Figure 2. Three radial maze tasks involving working memory or long-term memory consolidation.

  (A), (B) Behavioral performance (percentage of correct choices) across the two days of training in the three tasks, averaged by blocks of two sessions (A) or by day (B).Repeated-measure ANOVA 2 revealed significant effects of Groups, Blocks/Day and Groups x Blocks/Day factors. More specifically, performance in the LIWM group is higher than in HIWM and improves between the two training days, while HIWM performance stagnates at a lower level across the two days of training. Performance in the RM group is lower than in the WM groups at the beginning of training, but rats rapidly learn the rule and improve their performance across the two days of training, reaching a level of performance equivalent to the one observed in LIWM rats on Day 2.

Figure 3 .

 3 Figure 3. Working and reference memory tasks trigger opposed long-term synaptic changes with a local component. (A) fEPSP slope dynamics in the three groups during Baseline and Day 1 (post-training), averaged by 6h-periods and normalized to the averaged slope of the 24h of Baseline (dotted line). The repeated-measure ANOVA 2 did not reveal any Group of Time x Group significant effect. (B) Direct within-period comparison of the fEPSP slope (again normalized to the averaged slope on 24h of Baseline) between Baseline and the first day posttraining. The repeated-measure ANOVA 2 and post-hoc tests revealed (i) a significant early synaptic depression after LIWM training (during the first 3h post-training -ZT3-6) compared to the same period in Baseline, (ii) a significant late synaptic potentiation in the HIWM group starting 3h and ending 15h post-training (ZT6-12 -ZT12-18), and (iii) a significant late synaptic potentiation in the RM group starting 9h and ending 21h post-training (ZT12-18 -ZT18-24). (C) Between-electrode and within-animal variability of the fEPSP slope during Baseline and Day 1.Evolution of the mean standard deviation of the fEPSP slope across electrodes of individual rats by 6-hours periods on Baseline and Day 1, for the three behavioral groups. No significant differences were observed between groups on Baseline or on Day 1. (D) For each of the three behavioral groups, comparison of the mean standard deviation between Baseline and Day 1 in each 6-hours period. This between-electrode and within animal variability of the fEPSP slope was significantly increased in all periods of Day 1 compared to Baseline in the three groups, except for the first period ZT0-6 in the LIWM group. Data are represented as mean ± SEM.

Figure 4 .

 4 Figure 4. The recorded long-term synaptic changes are correlated with behavioral performance in the working and reference memory tasks. (A) Correlations between behavioral performance at the end of Day 1 (Block 5) and fEPSP slope recorded post-training during the four 6h-periods of Day 1 (normalized to the averaged fEPSP slope on 24h of Baseline). Note that good performance on B5 for LIWM rats predict synaptic depression during the first 3h post-training (negative correlation between past performance and fEPSP slope: r = -0.334, p = 0.0186 *), as illustrated by the scatter plot on the right (each dot representing one animal). (B) Correlations between fEPSP slope recorded post-training during the four 6h-periods of Day 1 (normalized to the averaged fEPSP slope on 24h of Baseline) and behavioral performance at the beginning of Day 2 (Block 6). Note that synaptic potentiation at the end of post-training Day 1 (more specifically at ZT12-18) for HIWM rats predicts bad behavioral performance on B6 (negative correlation between fEPSP slope and future performance: r = -0.672, p = 0.0128 *), as illustrated by the

Acknowledgments

This research was supported by grants from CNRS (ATIP program), Fondation pour la recherche sur le cerveau (FRC), and Région Rhône-Alpes (CIBLE program). N.F. was also supported by Fondation pour la recherche médicale (FRM-FDT20130928087) and Région Rhône-Alpes (ARC2 doctoral fellowship). We thank Fernand Malleret for his design and realization of the radial maze apparatus.

Declaration of interest

The authors declare no competing interests.


X Wang

C Prigent

Comparisons of diurnal variations of Land Surface Temperatures from Numerical Weather Prediction analyses, infrared satellite estimates and in situ measurements

Introduction

Land surface temperature (LST) plays a key role in the land surface energy budget. It is the radiative skin temperature and shows large spatial variabilities due to strong surface heterogeneity. The seasonal and diurnal cycle of LST is influenced by solar insolation, atmospheric changes (e.g., cloud coverage), and surface characteristics. LST and its diurnal variation are useful for a wide variety of applications. For example, they have been studied for the urban climate changes [START_REF] Pongracz | Remotely sensed thermal information applied to urban climate analysis[END_REF][START_REF] Hung | Assessment with satellite data of the urban heat island effects in asian mega cities[END_REF], land cover changes [START_REF] Sobrino | Toward remote sensing methods for land cover dynamic monitoring: application to morocco[END_REF], land-atmosphere interactions [START_REF] Anderson | Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources[END_REF][START_REF] Kustas | Advances in thermal infrared remote sensing for land surface modeling[END_REF][START_REF] Sun | Diurnal temperature range over the United States: A satellite view[END_REF], Zhang et al., 2016], fire monitoring [START_REF] Simon | Burnt area detection at global scale using ATSR-2: The GLOBSCAR products and their qualification[END_REF][START_REF] Barbosa | An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale[END_REF], droughts and water managements [START_REF] Karnieli | Use of NDVI and Land Surface Temperature for drought assessment: Merits and limitations[END_REF] etc.

Since ∼40 years, LST has been estimated from satellite thermal infrared (TIR) observations, at a global scale, from both geostationary and polar satellites. They use single channel algorithms, or split-window algorithms with temperature and emissivity separation methods. For instance, the International Satellite Cloud Climatology Project (ISCCP) produces global LST (in addition to many cloud variables), since 1982, from a large collection of geostationary and polar satellites observing in the infrared [START_REF] Rossow | Advances in understanding clouds from ISCCP[END_REF]. A new version of the products, 3-hourly and at a spatial resolution of ∼10km, has recently been delivered [START_REF] Young | The International Satellite Cloud Climatology Project H-Series climate data record product[END_REF], with a number of modifications applied to the previous D version. The EUMETSAT Land Satellite Applications Facility (Land SAF) also produces LST estimates from SEVIRI (Spinning Enhanced Visible and Infrared Imager) on board the geostationary satellite METEOSAT Second Generation, every 15 mn with a spatial resolution of 3 km [START_REF] Trigo | The satellite application facility for land surface analysis[END_REF]. It covers the continents of Europe, Africa and a part of south America and west Asia. The TIR technique has the advantages of producing data with fine spatial resolution, however they are constrained by atmospheric and cloud contamination.

Numerical Weather Prediction (NWP) centers combine observations and modeling resources in assimilation schemes to provide weather forecast and climate data records [START_REF] Dee | The ERA-Interim reanalysis: configuration and performance of the data assimilation system[END_REF]. ECMWF (European Centre for Medium-Range Weather Forecasts) produces the ERA series, with the latest ERA5 dataset providing hourly estimates of a large range of geophysical products (including LST), with an enhanced spatial resolution of 31 km, since 1979 [START_REF] Balsamo | A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System[END_REF]. They offer a detailed record of global land, atmosphere and ocean, by using the recent developments in model physics and and data assimilation, Several ground stations have been developed in response to the growing need for more in situ LST measurements for the validation of satellite observations and numerical models, and for climate and weather studies. NOAA's Surface Radiation Networks (SURFRAD) operate stations in the United States since 1993 [START_REF] Augustine | An update on SURFRAD-the GCOS surface radiation budget network for the continental united states[END_REF]. Other permanent stations are run by the Karlsruhe Institute of Technology (KIT) [START_REF] Gottsche | Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at gobabeb, namibia[END_REF]. These in situ measurements were used in several satellite validation studies [Trigo et al., 2008a, Gottsche et al., 2016[START_REF] Jiménez | Inversion of AMSR-E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature[END_REF].

In this work, we propose an evaluation of recently available LST datasets that sample the LST diurnal cycle (the new version of ISCCP and ERA5). Comparisons will also involve the Land SAF products from SEVIRI and in situ measurements. Data covering a full seasonal cycle in 2010 are selected. We will first evaluate model and satellite products at continental and regional scales, and then we will concentrate on comparisons with the in situ observations, under a large variety of environments. The paper is organised as follows. In Section 2 we present the datasets and their processing. In Section 3, comparisons are performed between ECMWF, ISCCP, and SEVIRI LSTs, within the disk covered by SEVIRI. The data and their diurnal variations are further evaluated with ground-based measurements in Section 4. Conclusions are provided in Section 5.

The LST datasets

The ERA reanalyses from NWP

ERA5 is the latest European reanalysis that replaces the ERA Interim product. It includes several important improvements with a revised land surface hydrology (HTESSEL) [START_REF] Balsamo | A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System[END_REF]. The revisions to the soil hydrology include: (i) a spatially varying soil type replacing the single global soil texture, (ii) a different formulation of soil hydraulic properties, and (iii) the surface runoff changes based on soil type and local topography. In addition ERA5 offers higher temporal and spatial resolutions compared to ERA Interim, i.e., hourly versus 6-hours, and 31 km in horizontal dimension versus 79 km. The ERA5 data can be found at http://apps.ecmwf.int/data-catalogues/era5 and here the ERA5 products have been downloaded gridded at 0.25 • spatial resolution. ERA Interim data is also used in this study, for comparison purposes, as many studies used this product so far. It is downloaded from http://apps.ecmwf.int/datasets/data/interim-full-daily, gridded at the same 0.25 • spatial resolution as ERA5.

The infrared ISCCP satellite product

The International Satellite Cloud Climatology Project (ISCCP) produces a global, calibrated infrared and visible radiance dataset, along with information on the radiative properties of the atmosphere and surface [START_REF] Rossow | Advances in understanding clouds from ISCCP[END_REF]. The primary instruments used by ISCCP are the imaging radiometers onboard the AVHRR and several geostationary satellites. ISCCP products use visible (VIS around 0.65µm) and infrared (IR around 10.5µm) window channels to derive cloud and surface parameters. The surface temperature under clear-sky condition is retrieved by the inversion of radiative transfer equations. A recent version of the ISCCP product (the H-series) is provided by the National Climate Data Center of NOAA [START_REF] Young | The International Satellite Cloud Climatology Project H-Series climate data record product[END_REF][START_REF] Rossow | Climate data record program (CDRP): Climate algorithm theoretical basis document (C-ATBD)[END_REF]. The data processing applies onboard target channel calibration and a sensor inter-calibration in order to produce multi-sensor radiance dataset. Compared to the former series (D-series), the H-series used improved atmospheric profiles, snow/ice cover fraction, surface type classification, and cloud detection scheme. The new data record is available 3-hourly and at 10 km spatial resolution (compared to 30 km before). The infrared emissivities adopted for the land surface temperature retrieval are fixed per surface types. The data are downloaded from http://https://www.ncdc.noaa.gov/isccp/isccp-data-access.

The infrared SEVIRI satellite product

The LSA SAF produces LST from SEVIRI/MSG observation with view zenith angle ranging from 0 o to 80 o at temporal sampling rate of 15 minutes, and a spatial resolution of about 3 km [START_REF] Trigo | The satellite application facility for land surface analysis[END_REF]. LST is obtained by correcting top-of-atmosphere radiances for surface emissivity, atmospheric attenuation, and downwelling radiations. It follows the generalized split window method developed by [START_REF] Wan | A generalized split-window algorithm for retrieving land surface temperature from space[END_REF] for AVHRR and MODIS satellites, but adapted to SEVIRI response functions. The LST is estimated as a linear function of clear-sky top-of-atmosphere brightness temperature measured by SEVIRI at 10.8 and 12.0µm. The regression coefficients have been estimated for classes of total column water vapor and satellite zenith angles. The emissivities are estimated as a weighted average of bare ground and vegetation elements within each pixel, with the fraction of vegetation cover directly generated from SEVIRI data [Trigo et al., 2008b]. The data are downloaded from http://landsaf.ipma.pt. The SEVIRI LSTs have been evaluated by comparison with satellites products (MODIS) and with in situ measurements, with Root Mean Square (RMS) differences between 1 K and 2.5 K [Trigo et al., 2008a[START_REF] Ermida | Validation of remotely sensed surface temperature over an oakwood landscape-the problem of viewing and illumination geometries[END_REF][START_REF] Freitas | Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat[END_REF].

The in situ measurements

The ESA GlobTemperature Data User Element (DUE) project compiled in situ measurements from stations, over several major land cover types (http:www.globtemperature.info). For meaningful comparisons between the in situ and satellite retrieved temperature, stations were selected for their rather homogeneous land surfaces within the sensor field-of-views. The stations are listed in Table 1, grouped by climate types. Three of them (EVO, GBB, and KAL) are run by the Land Surface Analysis Satellite Applications Facility (LSA SAF) [START_REF] Trigo | The satellite application facility for land surface analysis[END_REF] and have narrowband infrared radiometers dedicated to the validation of MSG/SEVIRI LST. The LST is estimated from a simplified radiative transfer model with an uncertainty of 0.8 ± 0.12K [START_REF] Gottsche | Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at gobabeb, namibia[END_REF]. The remaining seven (GCM, BND, PSU, SFA, FPK, DRA, and TBL) belong to the Surface Radiation Network (SURFRAD) [START_REF] Augustine | An update on SURFRAD-the GCOS surface radiation budget network for the continental united states[END_REF] and use broadband infrared radiometers that are part of a suite of different instruments dedicated to measure long time series of radiation-related variables. The LST is computed via the Stephan-Boltzmann law using measured upwelling and downwelling radiances, with overall uncertainty of 0.6-2 K.

Data preparation for comparisons

In this study, the LSTs are compared for 2010, over the four seasons (January-February, April-May, July-August, and October-November), and from four different products (summarized in Table 2). Our purpose is to evaluate the LST of these products at continental, regional and local scales, and then to quantify their diurnal and seasonal discrepancies. These products have different spatial and temporal resolutions and need to be carefully collocated for comparisons. In addition, the infrared satellite observations are strongly affected by clouds. The data preparation is described as follows. Firstly, SEVIRI data are aggregated to the ISCCP pixel, and SEVIRI cloud flag is adopted for all comparisons involving this instrument. If ISCCP data are available at the same time, the ISCCP cloud flag is also applied, reducing the number of matchups but limiting the possible cloud contamination. Then clear-sky infrared data from ISCCP and SEVIRI are averaged to the ERA5 grid of 0.25 • . The snow-covered pixels are kept in all the products. These data are then used in the large-scale LST comparisons in the below.

Large scale LST comparisons

In this section, we compare the LST from ERA5, ERA Interim, ISCCP, and SEVIRI, over the SEVIRI disk. The comparisons are made over the 0.25 • ERA grid. Figure 1 presents the differences ERA5-Interim, ERA5-ISCCP, and ERA5-SEVIRI at 00 and 12 UTC, averaged over months for two seasons (January-February and July-August). All data are available at 00 and 12 UTC, so these figures are representative of the general patterns of temperature differences without temporal interpolation. As expected, the two ECMWF products are close to each other, but some differences are nevertheless noted, especially over the inter-tropical zone during the day. During the daytime over arid and semi-arid regions, the infrared-derived ISCCP LST are clearly larger than the ERA5 LSTs, but the opposite prevails over dense vegetation. SEVIRI also tends to provide larger LSTs than ERA5 over arid regions during the day, but the difference is smaller than with ISCCP. Figure 2 presents the corresponding number of clear-sky collocations over the two time periods. Differences between the reanalysis and the infrared-derived satellite tend to increase when the number of collocations are reduced, during the cloudy seasons. For instance, the area of large discrepancies over the equator in Africa in January-February (Figure 1) correspond to large cloud coverage (Figure 2), and it moves northward with the Intertropical Convergence Zone (ITCZ) during July-August. Such seasonal cycle was also reported in previous studies [START_REF] Waliser | A satellite-derived climatology of the ITCZ[END_REF][START_REF] Nicholson | The seasonal evolution of the atmospheric circulation over west africa and equatorial africa[END_REF][START_REF] Nicholson | The ITCZ and the seasonal cycle over equatorial africa[END_REF]. Thus it is likely that cloud contamination still impacts the infrared LSTs, despite the stringent cloud clearing procedure.

The resulting collocated data are now sorted by local times. Four regions under different environments are selected (including two in situ stations). They are indicated in Figure 2: a rain forest location in Congo, the in situ Evora site in Portugal, shrubland in South Africa, and the in situ Gobabeb site in the Namibian desert. Each region has a size of 6 • (lat) x 6 • (lon). Figure 3 shows the diurnal mean temperature of the different products for these locations, as well as their difference with ERA5 (Figure 4), for the four seasons.

Regardless of the surface type, ERA Interim tends to produce a reduced amplitude of the diurnal cycle of LSTs, with lower LSTs at midday and slightly higher temperature during the night. This is partly due to the 6 h sampling of this product that can miss the maximum and minimum LSTs at local time. The diurnal cycle over dense vegetation (here rain forest) is limited, as expected. ERA5 produces larger LSTs than the other products in this region (including ERA Interim). [START_REF] Betts | Hydrometeorology of the amazon in ERA-40[END_REF] and [START_REF] Balsamo | A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System[END_REF] reported that both TESSEL and HTESSEL underestimate rainfall and reduce run off in tropical forest, leading to higher surface temperature in the reanalyses.

Over the other surface types, strong diurnal cycles and seasonal variations are observed in the LSTs, with rather limited differences between the products at night. Discrepancies between the reanalyses and the infrared estimates increase in the day, with a maximum close to the maximum LSTs (shortly after midday local time), and the reanalyses underestimating the maximum LSTs. ISCCP produces significantly larger LSTs during the day than the other estimates, especially under arid and semi-arid environments. This has already been observed in the previous versions of the ISCCP products [START_REF] Jiménez | A comparison of ISCCP land surface temperature with other satellite and in situ observations[END_REF].

Comparisons with in situ observations

In this section we use the in situ measurements to evaluate LST products. The in situ observations have high temporal (every 1 minute) resolution. The spatial representiveness of ground-based LST is within 100 m x 100 m at SURFRAD stations, and it may be more than 100 km at the KIT stations [Martin andGottsche, 2016, Gottsche et al., 2013]. The closest in space ECMWF reanalyses, ISCCP, and SEVIRI pixels are selected for the comparisons. The locations of all the stations and the corresponding satellite pixels (if available) are displayed in Figure 5. Concerning the surface around stations, the land cover near SFA, TBL and BND were reported to have concerns of heterogeneity, according to [START_REF] Li | Evaluation of 10 year AQUA/MODIS land surface temperature with SURFRAD observations[END_REF] and [START_REF] Guillevica | Validation of land surface temperature products derived from the visible infrared imaging radiometer suite (viirs) using ground-based and heritage satellite measurements[END_REF].

Emissivity assumptions from the in situ measurements and the satellite processing are different. GBB in situ station uses a constant emissivity 0.94 ±0.015, while the other KIT stations made use of the operational emissivity product from LSA-SAF. At the SURFRAD stations, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) provides the emissivity database at 0.05 • ×0.05 • [Seemann et al., 2008], used to compute the monthly broadband emissivities. Figure 6 shows the mean emissivities calculated from LSA-SAF for SEVIRI (originally estimated on a daily basis), for ISCCP (prescribed values according to surface types) and CIMSS (monthly mean). The emissivities show different behaviors in their values and in their seasonal patterns.

Figure 7 presents the diurnal LST cycles during three selected days at KIT stations for example. Around midday, the in situ measurements can have rapid variations: they are likely related to some clouds that shadow the incoming sun radiation. Figure 8 shows the mean diurnal variations of LST, averaged over two month periods under clear sky conditions. Good general agreement is observed among the products in this figure, with similar spatial, seasonal, and diurnal patterns. Some exceptions are observed in winter, for likely snow covered surfaces at BND, PSU and SFA, where ISCCP is systematically colder compared to the reanalyses and the in situ data when the temperature is below or close to 0 • C. ISCCP use a fixed emissivity of 0.99 for pixels that are identified as snow, but the large temporal and spatial variations of snow within ISCCP pixels may result in emissivity inaccuracies. Temperature retrieval can be also complex due to more difficulties in cloud identification over snow-covered surfaces, as noted by [START_REF] Rossow | Advances in understanding clouds from ISCCP[END_REF]. In arid and semi-arid regions, ISCCP tends to overestimate the LST at midday, as compared to the in situ measurements. The other products do not show such systematic disagreements with the in situ measurements.

The mean differences between the in situ measurements and satellite/reanalysis products are shown in Figure 9. The results of the KIT and the GCM stations are studied with more details in the below. Brief information is provided with station environment and climate, and more information can be found in [START_REF] Gottsche | Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at gobabeb, namibia[END_REF], [START_REF] Augustine | An update on SURFRAD-the GCOS surface radiation budget network for the continental united states[END_REF], and [START_REF] Wang | Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface long wave radiation observations at SURFRAD sites[END_REF].

GBB: Arid station in Namibia

The station is covered by highly homogeneous mixture of gravel, sand and sparse desiccated grass. A sharp transition between sand dunes and the gravel plains is shown in Figure 5. SEVIRI pixels are on the gravel plains only, while ISCCP pixel covers both gravel and sand dunes. It has a hot and arid climate, with the rainy season in January-March.

SEVIRI exhibits excellent agreement with the in situ, especially during the cool seasons (Apr-May and Jul-Aug), with absolute bias less than 1K. During the hot and rainy season (Jan-Feb), SEVIRI night temperature is about 2K lower than the in situ LST, likely due to cloud contamination in the satellite retrieval as mentioned in Trigo et al. [2008a]. During daytime in Oct-Nov, SEVIRI LST tends to be overestimated compared to the in situ (though less than ISCCP), and this is partially due to emissivity errors under arid conditions [Trigo et al., 2008a]. ISCCP agrees well to the in situ measurements at night, but it overestimates LST near midday.

Concerning the two ECMWF reanalyses, ERA Interim can overestimate nighttime temperatures by about 4 K while ERA5 has absolute bias less than 1 K often. Smaller bias of ERA5 can be attributed to the new diffusivity and conductivity model of HTESSEL, as the new model can reduce the overestimation of soil moisture in barren soil [START_REF] Balsamo | A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System[END_REF], Albergel et al., 2012].

KAL: shrubland station in Namibia

This station is located in a farm and the surface is a mixture of grass and sand. When more rain comes in January-March, grass is abundant; but in other seasons grass may dry out quickly as reported by [START_REF] Gottsche | Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in africa[END_REF]. ISCCP covers more sandy areas than SEVIRI (Figure 5).

Though remote sensed LST shows good coherence with the in situ at night, SEVIRI and ISCCP tend to overestimate temperature near midday. The overestimation is much less during the rainy season when grass is abundant. Concerning the two ECMWF reanalysis, they fit quite well the in situ LST during the rainy seasons (Jan-Feb), while some overestimation appears in dryer seasons, often at night. Compared to GBB, the error sources here may be more complex due to surface vegetation variations in space and time.

EVO: Mediterranean woody savanna

This station is located in woodland plains in Portugal. The climate is characterized by hot and dry summer and cold and rainy winter. The in situ temperature is a weighted average from three infrared radiometers, observing the sunlit background, a tree crown and the sky at 53 • zenith angle [Trigo et al., 2008a[START_REF] Ermida | Validation of remotely sensed surface temperature over an oakwood landscape-the problem of viewing and illumination geometries[END_REF][START_REF] Gottsche | Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at gobabeb, namibia[END_REF]. The emissivity is the same of station and of SEVIRI retrieval. ISCCP may cover more dense forest than SEVIRI.

SEVIRI has the best diurnal agreement in rather cold seasons, ie., Jan-Feb and Apr-May, with absolute bias less than 1K. More differences appear in summer daytime, with remote-sensed temperature higher than the in situ. This can be partially due to high variabilities of temperature and emissivity between tree canopies and the ground, which may result in larger uncertainty in the in situ observations, as also mentioned in previous studies [START_REF] Ermida | Validation of remotely sensed surface temperature over an oakwood landscape-the problem of viewing and illumination geometries[END_REF][START_REF] Freitas | Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat[END_REF][START_REF] Trigo | The satellite application facility for land surface analysis[END_REF].

GCM: subtropical station in the US

It is located in subtropical forest mixed with cropland, and the surface is rather homogeneous [START_REF] Guillevica | Validation of land surface temperature products derived from the visible infrared imaging radiometer suite (viirs) using ground-based and heritage satellite measurements[END_REF]. The two ECMWF reanalyses show higher/lower temperature in night/day than in situ measurements for all seasons. In the work of Albergel et al. [2012], modelled soil moisture were compared to in situ observations provided by the NRCS-SCAN (Natural Resources Conservation Service Soil Climate Analysis) Network in the United States. They found that HTESSEL estimates more soil moisture than the ground measurements, especially over vegetated surface such as forest, grass and cropland (which happens to TESSEL as well). Thus the overestimation of soil moisture may be an important reasons to the diurnal LST differences compared to the in situ.

For the two reanalyses, over(under)estimation at night(day) were observed in transitional areas such as grassland, cropland and forest, for example at stations of GCM, FPK and PSU etc. In these zones, a strong coupling exists between soil moisture and atmosphere which can influence temperature [START_REF] Catalano | Observationally based analysis of land-atmosphere coupling[END_REF][START_REF] Koster | Glace: The global land-atmosphere coupling experiment. part i: Overview[END_REF]]. In the reanalyses, soil moisture, aerodynamic (Zom) and thermal (Zoh) roughness length are directly linked to evaporation, hence to the LST. The two roughness lengths have fixed values at a given surface type, and this can result in some errors compared to observations [START_REF] Mitchell | The multiple-institution north america land assimilation system(NLDAS): utilising multiple GCIP products and partners in a continental distributed hydrological modelling system[END_REF]. Previous studies have shown that these two roughness lengths vary with vegetation and may have seasonal cycle [Zeng andWang, 2007, Zheng et al., 2014]. Recently their diurnal variations were studied in several regions by field observations and modelling [START_REF] Zhang | Assessment of roughness length schemes implemented within the noah land surface model for high-altitude regions[END_REF][START_REF] Zhou | Seasonal, diurnal and wind-direction-dependent variations of the aerodynamic roughness length in two typical forest ecosystems of china[END_REF][START_REF] Park | Roughness length of water vapor over land surfaces and its influence on latent heat flux[END_REF]. For example [START_REF] Zhou | Seasonal, diurnal and wind-direction-dependent variations of the aerodynamic roughness length in two typical forest ecosystems of china[END_REF] found the diurnal variations with Zom being larger at night and smaller during the day in mid-latitude forest. [START_REF] Pitman | Assessing the sensitivity of a land-surface scheme to the parameter values using a single column model[END_REF] also pointed out that 30% of Zom error can result in 15% uncertainty in fluxes calculations. Underestimation of Zom (at night) means less surface-atmosphere coupling, thus higher surface temperature. Overestimation (at daytime) means more surface-atmosphere interactions and lower LSTs. High/low Zoh can also over/under estimate sensible and latent heat flux, leading to lower/higher surface temperature, as pointed by [START_REF] Zhang | Assessment of roughness length schemes implemented within the noah land surface model for high-altitude regions[END_REF]. Thus, the adjustment of roughness length values with temporal variations may be useful to improve the model processes at the surface and the LST estimation, as also suggested by [START_REF] Trigo | Comparison of model land skin temperature with remotely sensed estimates and assessment of surface-atmosphere coupling[END_REF].

Figure 10 summarizes the statitics over all the seasons, ie., correlation with the in situ measured LST, mean and root-mean-square (RMS) of differences compared to ground data. All the products have good correlations of more than 0.95 (though less for ISCCP at some stations). SEVIRI has smaller RMS and larger correlation than the other three products, which means very good agreement to the in situ measurements, knowing that their retrievals have been validated at the KIT stations. ISCCP has more RMS and less correlations in almost all the stations, particularly on snow influenced surface, that is., PSU, BND, SFA and FPK. The two ECMWF reanalyses have almost equivalent correlations and RMS.

Conclusions

This study analyzed the diurnal variations of LST estimates from ECMWF reanalyses (ERA5 and ERA Interim), from infrared satellite instruments (ISCCP, SEVIRI), and from in situ measurements. Clear-sky data from four seasons in 2010 were analyzed. Firstly we compared satellite and reanalysis data within the SEVIRI disk. Then, the comparison focused on in situ measurements at 10 dedicated ground stations.

Spatial, seasonal and diurnal variations of LST are compared. In general more differences are seen in warm seasons than cold ones, more at midday than at night. At the three KIT stations, SEVIRI shows better agreement with the in situ measurements than the other products (in bias and correlation), with bias often less than ±2K and correlation of 0.99. Over snow or arid surface ISCCP seems to have more systematical errors than the other products (no SEVIRI data are available over snow-influenced stations). The validation of satellite retrievals can be still influenced by cloud contamination, surface heterogeneity, and emissivity inaccuracies, despite the stringent cloud clearing and collocation procedures.

Regardless of the surface type, ECMWF ERA5 tends to produce a larger diurnal cycle of LSTs, with higher LSTs at midday and slightly lower temperature during the night. Over barren land, ERA5 shows better agreement to the in situ than ERA Interim, particularly at night time, thanks to the new surface model. Over vegetated surface, both models tend to have higher/lower temperature at night/day time than the in situ measurements. This can be linked to the overestimation of soil moisture and the limitations in surface-atmosphere coupling in the models.

Figure 1 :

 1 Figure 1: Monthly mean differences of LST (unit: K) for ERA5-ERA Interim, ERA5-ISCCP, and ERA5-SEVIRI at 00 UTC and 12 UTC of two seasons, ie., January-February, July-August in 2010.

Figure 3 :

 3 Figure 3: Diurnal LST variations (unit: K) for ERA5, ERA Interim, ISCCP, and SEVIRI over 4 selected vegetation types in Jan-Feb, Apr-May, Jul-Aug, and Oct-Nov in 2010.

Figure 4 :

 4 Figure 4: Diurnal LST bias variations (unit: K) for ERA5-ERA Interim, ERA5-ISCCP, ERA5-SEVIRI over 4 selected vegetation types in Jan-Feb, Apr-May, Jul-Aug, and Oct-Nov in 2010.

Figure 5 :

 5 Figure 5: The locations of the ground stations and corresponding satellite pixel when available. Black: ISCCP pixel. Green: SEVIRI. Yellow: ground station.

Figure 6 :

 6 Figure 6: Emissivities used at the ground station location in the LST retrieval from ISCCP and SEVIRI (when available), and from the estimates of CIMSS. Black: ISCCP. Green: SEVIRI. Yellow: CIMSS.

Figure 7 :

 7 Figure 7: Example observations at GBB, KAL and EVO from ERA5, ERA Interim, ISCCP, SEVIRI, and in situ respectively in 2010.

Figure 8 :

 8 Figure 8: Diurnal variations of monthly mean LST (unit: K) over selected stations, from ERA5, ERA Interim, ISCCP and SEVIRI (when available), and in situ measurements. X axis is local hour.

Figure 9 :

 9 Figure 9: Diurnal variations of monthly mean differences of LST (unit: K) at selected stations, ie., In situ -ERA5, In situ -ERA Interim, In situ -ISCCP and In situ -SEVIRI (when available). X axis is local hour.

Figure 10 :

 10 Figure 10: a) Correlation of LST products to the in situ measurements at each station over all the seasons. b) Mean of the differences in comparison to the in situ measurements at each station over all the seasons. c) Root-mean-square (RMS) of the differences in comparison to the in situ measurements each station over all the seasons.

Table 1 :

 1 Characteristics of the in situ stations measuring the LST.

	ID (longitude, latitude) Location		Climate	Surface type
	GCM (-89.9, 34.2)	Goodwin Creek, Mississippi, USA	Humid subtropical Forest, grassland
	PSU(-77.9, 40.7)	Pennsylvania State University, Pennsylvania, USA Humid continental Forest, cropland
	BND (-88.4, 40.0)	Bondville, Illinois, USA		Humid continental Cropland
	SFA (-96.6, 43.7)	Sioux Falls, South Dakota, USA	Humid continental Cropland, grassland
	FPK (-105.1, 48.3)	Fort Peck, Montana, USA	Semiarid	Cropland, grassland
	TBL (-105.2, 40.1)	Table Mountain, Boulder, Colorado, USA	Highland	Rocks, sparse grassland
	DRA (-116.02, 36.62)	Desert Rock, Nevada, USA	Arid	Arid shrubland
	EVO (-8.00, 38.5)	Evora, Portugal		Mediterranean	Savanna, woody savanna
	GBB (15.05, -23.55 )	Gobabeb, Namibia		Arid	Bare ground
	KAL (18.3, -23.0)	Kalahari, Namibia		Arid	Shrubland
	Product	Spatial coverage Spatial resolution Temporal resolution References
	ECMWF ERA5	global	0.25 •	1 hour	Balsamo et al. [2009]
	ECMWF ERA Interim global	0.25 •	6 hours	Berrisford et al. [2011]
	ISCCP	global	0.1 •	3 hours	Rossow [2017]
	SEVIRI	regional	3 km	15 minutes	Trigo et al. [2015]

Table 2 :

 2 Characteristics of the LST products from the ECMWF reanalyses and from the infrared satellites.