This study presents regional simulations over the Iberian Peninsula between 2010 and 2022 with the atmospheric (ICOLMDZ) and land surface (ORCHIDEE) components of the IPSL climate model in a new limited area model configuration (25 km resolution). It uses a recently developed river routing and irrigation scheme based on a water-conservative supply-and-demand approach. Two simulations, with and without irrigation, are compared to isolate the impacts of simulated irrigation on land-atmosphere interactions and the water cycle. First, an evaluation of the simulations is conducted to characterize existing model biases in river discharge, precipitation, evapotranspiration (ET) and surface soil moisture (SSM), and assess whether they can be improved by simulating irrigation. The simulated irrigation is too low in southern Spain because of a lack of available water in the reservoirs, and likely because of the absence of representation of river dams. In northern regions such as the Ebro Valley, the simulated irrigation is more realistic and reduces the biases of river discharge and ET in summer and autumn. In general, SSM is not strongly impacted by irrigation as most additional water is evaporated. Second, atmospheric changes induced by irrigation are studied in summer (JJA). Large atmospheric responses are found over intensely irrigated areas, mainly consisting of a shift in energy partitioning between the turbulent fluxes (increase in latent heat flux and decrease in sensible heat flux, up to 50 W m−2), and a lowering of the atmospheric boundary layer (−100 m) and of the lifting condensation level (−250 m). Increases in precipitation are statistically significant only over the mountainous areas surrounding the Ebro Valley, and are closely linked to increases in convective available potential energy. Finally, atmospheric moisture recycling over the Iberian Peninsula is identified by showing that the increase in ET in the presence of irrigation exceeds the amount of water added by irrigation. This is made possible by an increase in precipitation over land, although most of this increase is located in lightly irrigated areas rather than in intensively irrigated areas. These results point to remote atmospheric effects of irrigation and motivate further investigation into land-atmosphere coupling processes in the presence of irrigation in the IPSL model.

Run-time bias corrections of atmospheric circulation models can be based on nudging (Newtonian relaxation) to an atmospheric reanalysis. In this case, the time increments of selected state variables are modified by adding the nudging terms obtained with an uncorrected version of the model. This is a well-known method to improve the models' representation of large-scale circulation patterns. In this work, we propose and evaluate a variant of this method, consisting of iterative nudging: the corrected model is itself nudged towards the reanalysis, and the resulting nudging terms are added to the initial ones to calculate the new, iterated correction terms. This procedure can be iterated an arbitrary number of times. Evaluating the LMDZ atmospheric general circulation model (AGCM) for a varying number of iterations of nudging to the ERA5 reanalysis for the period 1981-2000, we show that the simulated large-scale circulation patterns over the period 2001-2020 are consistently improved when the bias correction procedure is iterated compared to the non-iterated original procedure. However, signs of over-correction appear after about three iterations.

Irrigation is one of the main human landscape management activity, and has seen a dramatic increase during the XXth century, with a direct local increasing effect on soil moisture (SM) and evapotranspiration (ET). To sustain the increase of ET, irrigation withdraws water from rivers and groundwater reservoirs. As a result, irrigation activities have a direct impact on water and energy balance, and drive the evolution of ET under the ongoing climate change in intensively irrigated regions. On the other hand, irrigation induces feedbacks from the atmosphere, that includes changes in precipitation patterns, air temperature cooling and changes in energy-related variables. Moreover, future climate change will complexify these interactions, and it is not clear what would be the future implications of irrigation activities in water resources management and key hydroclimate variables. To assess the joint evolution of irrigation, water resources and hydroclimate variables, we use an irrigation scheme that was recently evaluated in ORCHIDEE, the land surface component of the IPSL climate model. This scheme calculates water demand based on a soil moisture deficit approach, and restrains water supply to water available in small and large rivers and in groundwater, considering the facility to access the water source and an environmental volume for ecosystems. To assess the effect of irrigation on water resources and climate, we use two transient coupled simulations at global scale, for the period 1950-2100, under SSP5-RCP8.5 scenario to have a strong climate change signal during the future period. One of the simulations runs with the irrigation scheme activated, while the second one runs without irrigation. Preliminary results at global scale show that irrigation will increase under the chosen scenario, due to the prescribed increase of the irrigated surface from scenario SSP5-RCP8.5 and a warmer climate. This increase will counteract part of the increasing trend of groundwater storage and will complexify the evolution of river storage in irrigated areas. On the other hand, it will enhance the increase of ET at global scale. We will extend our analysis to water and energy-related variables, including key climate variables like precipitation and air temperature, at different seasons and regions. We will also focus our analysis in some intensively irrigated areas, to assess the causes of possible water supply shortages in irrigation activities. These results should help to understand future implications of irrigation in water resources management in irrigated areas, and also effects in non-irrigated zones via remote land-atmosphere feedbacks.

Abstract. This study presents regional simulations over the Iberian Peninsula between 2010 and 2022 with the atmospheric (ICOLMDZ) and land surface (ORCHIDEE) components of the IPSL climate model in a new limited area model configuration (25 km resolution). It uses a recently developed river routing and irrigation scheme based on a water-conservative supply-and-demand approach. Two simulations, with and without irrigation, are compared to isolate the impacts of simulated irrigation on land surface-atmosphere interactions and the water cycle. First, an evaluation of the simulations is conducted to characterize existing model biases in river discharge, precipitation and evapotranspiration (ET), and assess whether they can be improved by simulating irrigation. The simulated irrigation is too low in southern Spain because of a lack of available water in the reservoirs, and likely because of the absence of representation of river dams. In northern regions such as the Ebro Valley, the simulated irrigation is more realistic and reduces the biases of river discharge and ET in summer and autumn. Second, atmospheric changes induced by irrigation are studied in summer (JJA). Large atmospheric responses are found over intensely irrigated areas, mainly consisting of a shift in energy partitioning between the turbulent fluxes (increase in latent heat flux and decrease in sensible heat flux, up to 50 W m⁻²), and a lowering of the atmospheric boundary layer (−100 m) and of the lifting condensation level (−250 m). Increases in precipitation are statistically significant only over the mountainous areas surrounding the Ebro Valley, and are closely linked to increases in convective available potential energy. Finally, atmospheric moisture recycling over the Iberian Peninsula is identified by showing that the increase in ET in the presence of irrigation exceeds the amount of water added by irrigation. This is made possible by an increase in precipitation over land, although most of this increase is located in lightly irrigated areas rather than in intensively irrigated areas. These results point to remote atmospheric effects of irrigation and motivate further investigation into surface-atmosphere coupling processes in the presence of irrigation in the IPSL model.

The recent years have shown increasing interest and effort to include simulation of irrigation in Earth System Models to better account for the effects of this anthropogenic process on climate. We present here preliminary results about the impacts of simulated irrigation on surface-atmosphere interactions using LMDZ and ORCHIDEE, the atmosphere and land surface components of the IPSL Climate Model. The DYNAMICO-LMDZ configuration, coupling the physics of LMDZ to the recent icosahedral dynamical core DYNAMICO, is run as a Limited Area Model (LAM) to conduct a regional study over North-Eastern Spain. The simulation domain encompasses the Ebro valley where the LIAISE (Land-surface Interactions with the Atmosphere In Semi-Arid Environment) field campaign was conducted in 2021. This campaign was specifically designed to provide better understanding of the local and regional impacts of irrigation and the surface heterogeneities it creates. A new representation of irrigation, based on a soil moisture deficit approach, has recently been developed in ORCHIDEE and simulations are run with and without it to assess the impacts of simulated irrigation in the model. Direct effects at the land-surface interface (soil moisture, turbulent fluxes, temperature) are studied first, before focusing on the structure of the boundary layer and precipitations. Field observations from the campaign are used to evaluate the model, and the outputs will also be compared to higher-resolution simulations that have been conducted using the Meso-NH model in the context of the LIAISE project. The impacts of irrigation will be studied using various resolutions of the LAM from 10 to 50km, to better understand the scales at which land-surface coupling processes can be explicitly resolved by the dynamics of the model, and assess the importance of parametrizating these processes.

Irrigation, a key activity for food security, uses local water resources to increase evapotranspiration, creating feedback loops with the atmosphere and water resources. With climate change, it is unclear how irrigation will evolve in the future and how it may influence the evolution of water resources and the water cycle. It is also unclear whether irrigation may be constrained by climate change or water resource shortages. Here, we compare two surface-atmosphere simulations performed with the IPSL-CM6 model from 1950–2100: one with irrigation and one without irrigation. In both simulations, the evolutions of atmospheric radiative forcing, land use, and irrigated areas are taken from CMIP6, which uses a historical dataset for the data before 2014 and the SSP5-RCP8.5 dataset for data after 2014. The two simulations reveal strong global warming and precipitation increases between 1950–2000 and 2050–2100 average values (+5.6 °C and +8.1 %, on average, over land with irrigation). Over the same period, our results indicate an increase in irrigation (+76 % increase in irrigation in the 2050–2100 compared to the 1950–2000 period), which is in line with an important expansion of irrigated areas. The influence of irrigation on evapotranspiration in irrigated areas is greater in 2050–2100 than in 1950–2000 (+12 % vs. +8 %, respectively). Evapotranspiration has also been found to increase in non-irrigated areas near irrigated zones owing to an increase in precipitation under historical and future climate conditions. Water depletion due to irrigation is more intense in the future than in the historical period, although climate change increases water storages and river discharge due to more precipitation in the future. We also identified areas where future environmental conditions can limit irrigation or where irrigation can increase tensions over water use (approximately one-third of irrigated areas, including the Mediterranean basin, California, and Southeast Asia). Our results highlight the importance of considering irrigation in climate projections and future water resources assessments.

The insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface-atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface-atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall. Without insulation, the latent heat needed for soil ice thawing leads to a late spring and summer cold bias in surface air temperature (above 2 °C) over Arctic regions. The integration of soil insulation eliminates this bias and significantly improves the simulation of permafrost dynamics. Our findings, including the potential consequences of large perturbations (e.g., fires), highlight the importance of combining soil water freezing with a physical representation of SOC and surface organic layer insulation in Earth system models, to improve Arctic climate predictions.

Une très grande variété de climats existe sur la Terre et des méthodes dédiées sont nécessaires pour comprendre leur variabilité et évolution dans un contexte de réchauffement climatique global. Au LMD, sont développées des approches numériques pour étudier les climats régionaux. Il s’agit historiquement du modèle global zoomé LMDZ et plus récemment du modèle régional du système Terre Reg-IPSL. Ces modèles ont permis l’étude de climats de nombreuses régions du globe, telles que l’Afrique de l’Ouest, la Méditerranée, l’Amérique du Sud, l’Asie, l’Antarctique et bien d’autres. Seules les études sur la région Méditerranée ont appliqué et comparé toutes les méthodes de régionalisation développées au laboratoire et sont développées dans cet article.

Morocco, as a Mediterranean and North African country, is acknowledged as a climate change hotspot, where increased drought and related water resource shortages present a real challenge for human and natural systems. However, its geographic position and regional characteristics make the simulation of the distribution and variability of precipitation particularly challenging in the region. In this study, we propose an approach where the Laboratoire de Meteorologie Dynamique Zoom (LMDZ) GCM is run with a stretched grid configuration developed with enhanced resolution (35 km) over the region, and we apply run-time bias correction to deal with the atmospheric model's systematic errors on large-scale circulation. The bias-correction terms for wind and temperature are built using the climatological mean of the adjustment terms on tendency errors in an LMDZ simulation relaxed towards ERA5 reanalyses. The free reference run with the zoomed configuration is compared to two bias-corrected runs. The free run exhibits noticeable improvements in mean low-level circulation, high frequency variability and moisture transport and compares favourably to precipitation observations at the local scale. The mean simulated climate is substantially improved after bias correction w.r.t. to the uncorrected runs. At the regional scale, the bias-correction showed improvements in moisture transport and precipitation distribution, but no noticeable effect was observed in mean precipitation amounts, interannual variability and extreme events. To address the latter, model tuning after grid refinement and developing more "scale-aware" parameterizations are necessary. The observed improvements on the large-scale circulation suggest that the run-time bias correction can be used to drive regional climate models for a better representation of regional and local climate. It can also be combined with "a posteriori" bias correction methods to improve local precipitation simulation, including extreme events. The LMDZ AGCM is used in a refined grid configuration to investigate the impact of resolution enhancement and run-time bias correction on the simulation of the regional precipitation over Morocco. Six model simulations are compared to in-situ observations, ERA5 reanalysis and a range of gridded observation datasets for the period 1979-2014. The bias corrected refined-grid runs show improvements in both general circulation and regional precipitation variability and distribution. image

Land surface-atmosphere interactions are a key component of climate modeling. They are particularly critical to understand and anticipate the climate and the water resources over the semiarid and arid North African regions. This study uses in situ observations to assess the ability of the IPSL-CM global climate model to simulate the land-atmo- sphere interactions over the Moroccan semiarid plains. A specific configuration with a grid refinement over the Haouz Plain, near Marrakech, and nudging outside Morocco has been performed to properly assess the model's performances. To ensure reliable model-observation comparisons despite the fact that station measurements are not representative of a mesh -size area, we carried out experiments with adapted vegetation properties. Results show that the CMIP6 version of the model's physics represents the near -surface climate over the Haouz Plain reasonably well. Nonetheless, the simulation exhibits a nocturnal warm bias, and the wind speed is overestimated in tree -covered meshes and underestimated in the wheat -covered region. Further sensitivity experiments reveal that LAI-dependent parameterization of roughness length leads to a strong surface wind drag and to underestimated land surface atmosphere thermal coupling. Setting the roughness heights to the observed values improves the wind speed and, to a lesser extent, the nocturnal temperature. A low bias in latent heat flux and soil moisture coinciding with a pronounced diurnal warm bias at the surface is still present in our simulations. Including a first -order irrigation parameterization yields more realistic simulated evapotranspiration flux and daytime skin surface temperatures. This result raises the importance of accounting for the irrigation process in present and future climate simulations over Moroccan agricultural areas.

This study presents the development of a TKE-l parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large-scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and their number minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has also been paid to the numerical stability at typical time steps used in General Circulation Models. Test, parametric sensitivity assessment and preliminary tuning are performed on single-column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are also assessed through its implementation in the LMDZ General Circulation Model and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

AbstractThis study presents the development of a so‐called Turbulent Kinetic Energy (TKE)‐l, or TKE‐l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large‐scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single‐column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

The Mediterranean basin and Northern Africa are projected to be among the most vulnerable areas to climate change. This research documents, analyzes, and synthesizes the projected changes in precipitation P, evapotranspiration E, net water supply from the atmosphere to the surface P-E, and surface soil moisture over these regions as simulated by 17 global climate models from the sixth exercise of the Coupled Model Intercomparison Project (CMIP6) under two Shared Socioeconomic Pathways, SSP2-4.5, and SSP5-8.5. It also explores the sensitivity of the results to the chosen climate scenario and model resolution and assesses how the projections have evolved from the fifth exercise (CMIP5). Models project a statistically robust drying over the entire Mediterranean and coastal North Africa. Over the Northern Mediterranean sector, a significant precipitation decrease reaching similar to 0.4 +/- 0.1 mm day similar to 1 is projected during the 21st century under the SSP5-8.5 scenario. Conversely, a significant increase in precipitation of +0.05 to 0.3 +/- 0.1mmday(-1) is projected over South-Eastern Sahara under the same scenario. Evapotranspiration and soil moisture exhibit decreasing trends over the Mediterranean basin and an increase over the Sahara for both SSPs, with a notable acceleration from the 2020s. As a result, P-E is projected to decrease at a rate of about -0.3mmday(-1) under the high-end scenario SSP5-8.5 over the Mediterranean whilst no significant changes are expected over the Sahara due to evapotranspiration compensation effects. CMIP6 and CMIP5 models project qualitatively similar patterns of changes but CMIP6 models exhibit more intense changes over the Mediterranean basin and South-Eastern Sahara, especially during winter.

Abstract The representation of heatwaves (HWs) in the Coupled Model Intercomparison Project phase 6 (CMIP6) models is analyzed. This study (a) evaluates the performance of CMIP6 simulations against global reanalysis and observations regarding time‐ and intensity‐related criteria and (b) investigates how HWs are projected to change at different global warming levels (GWLs). During 1979–2014, the dispersion of the models is comparable to the observational uncertainty for the time indices (duration, frequency, number of events). It is of the order of one event per year, 1 day for the duration of the events and 2 days for the frequency, with tendencies for over‐ or underestimation, depending on the reference data set and the region considered. For the HW magnitude, the models' dispersion can reach 15°C for a given region and is significantly higher than the observational uncertainty. The mean intensity of HWs tends to be overestimated, which is partly attributed to overly pronounced drying of the soil during HW events. The contribution of the soil moisture anomaly to the temperature anomaly during recent specific HWs is shown to reach up to 30% of the signal. For a given GWL, intensification of HW occurrence, spatial extension, and duration is detected worldwide, but it is modulated at the regional scale and strongly model dependent. For time‐related indices, tropical regions and the Arabian Peninsula will be most impacted, but the maximum temperature will strongly increase in mid‐latitude regions. Time–space analyses of the evolution of HW properties for a given GWL are discussed.

Abstract. The influence of topography on the snow cover fraction (SCF) is investigated in this study with 5 different parameterizations. These SCF parameterizations are evaluated using the High Mountain Asia Snow Reanalysis (HMASR). Then, they are implemented in the ORCHIDEE land surface model (LSM) of the Institut Pierre Simon Laplace (IPSL) general circulation model (GCM) to quantify their skill in global land-atmosphere coupled simulations. SCF varies as a function to snow depth (SD), with a relationship that differs between flat and mountainous areas in HMASR. SCF parameterizations that do not include a dependency on the topography lead to large snow cover overestimations. Furthermore, a hysteresis between SCF and SD is found in HMASR, with a rapid snow cover increase during accumulation and a slower retreat of patchy snow occurring during ablation periods, discarding parameterizations not considering this effect. The application of the parametrizations in global simulations shows contrasting results depending on the location because other processes also explain the snow biases. Nevertheless, the snow cover overestimation in mountain areas is reduced by about 5 to 10 % on average when we include a dependency on the subgrid topography in our SCF parameterizations, which in turn allows to decrease the surface cold bias from −1.8 °C to about −1 °C in the High Mountain Asia (HMA) region. However, persisting snow cover biases remain in these experiments, with a SCF overestimation in HMA, as well as a SCF underestimation in several other regions (e.g., the Rockies mountains). Further calibration considering other regions and multiple datasets would allow to improve the SCF parameterizations. Assessing SCF parameterizations is challenging as it requires both realistic snowfall and snowpack in model experiments, and combined SCF, SD, and SWE – or snow density – observations, that are generally limited and uncertain in mountainous regions.

The Amazon rainforest is a vital component of the hydrological cycle of South America. Its evapotranspiration is an essential supply of atmospheric moisture for precipitation in more southern regions of the continent. The potential impacts of deforestation on precipitation in these distant regions are yet not fully understood.The present research project aims at quantifying the deficit of evapotranspiration occurring at the location of deforestation, focusing on the southern part of Amazonia, which has experienced intense deforestation since the 80s. We first use the ORCHIDEE land surface model forced with the reanalysis dataset CRU-JRA to simulate the impacts of an imposed land cover change: from observed states of vegetation cover to a massive extension of croplands. The ORCHIDEE model computes all the components of evapotranspiration, giving, in turn, the expected deficit of atmospheric moisture at the location of the land cover change.Then, thanks to existing datasets connecting any place on Earth with the area that supplies its moisture through the atmosphere, we link this deficit with downwind locations highly dependent on this upwind evapotranspiration for its precipitation. From there, we draw hypotheses about the potential changes in precipitation amounts and seasonality.In the project’s second phase, these hypotheses are tested against land-atmosphere coupled simulations produced with the IPSL global climate model, nudged to winds from the ERA5 reanalysis. The model grid is zoomed on the South American continent to better describe the atmospheric transport in the region. The land-atmosphere coupled simulations provide information on the atmospheric feedback induced by the land cover change, confirming or invalidating the hypotheses. Since land cover affects not only water fluxes but also energy fluxes, the coupled model experiments give us insights into the atmospheric processes at stake, the changes in cloudiness and local convection, and the potential shifts in precipitation location or timing.

Permafrost soils located in high latitudes contain about 1500 petagrams of carbon. The strong and rapid warming of the Arctic climate threatens this important carbon stock. Permafrost thaw exposes previously frozen organic matter to decomposition by microorganisms, resulting in CO2 and CH4 emissions into the atmosphere that contribute to strengthening the initial warming. On the other hand, rising atmospheric CO2 concentration increases vegetation primary productivity in a feedback known as fertilization effect. In addition, the melting of permafrost may also likely provide more nitrogen in the soil that could stimulate plant growth. The balance between these competing processes is thought to be the primary driver of future permafrost carbon stocks. However, both the amplitude and timing of future net carbon emissions of permafrost areas remain highly uncertain. Reducing the uncertainty on net carbon balance in high latitudes would help improve the accuracy of carbon budget, and thus would impact political and social decisions towards the net zero target.Up to then, the impact of different future climate scenarios (RCP, SSP or similar) on permafrost have been largely explored with offline Land Surface Model simulations but feedbacks with the atmosphere and ocean cannot be represented in such configurations. Using the IPSL Earth System Model that couples the atmosphere, the ocean and continental surfaces (i.e., ORCHIDEE model), the future evolution of climate-carbon feedbacks in permafrost regions is assessed. In particular, the feedback between climate change and the carbon cycle and the fertilization effect are analyzed with the C4MIP formalism (γ and β parameters). A particular focus is put on the role of the nitrogen cycle through its interactions with the carbon cycle. In addition, the impact of soil insulation by soil organic carbon and surface mosses on thermal transfers is also analyzed in the context of coupled land - atmosphere simulations. The importance of surface insulation i) to maintain realistic surface air temperature, especially during spring time, avoiding large surface - atmosphere feedbacks with unrealistic cooling in the arctic and ii) to sustain large permafrost extent (close to observations) is highlighted.