Model Intercomparison Project Research Articles

Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data

Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.

Opinion: The importance of historical and paleoclimate aerosol radiative effects

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

Meridional Heat Transport in the DeepMIP Eocene Ensemble: Non‐CO2 and CO2 Effects

AbstractThe total meridional heat transport (MHT) is relatively stable across different climates. Nevertheless, the strength of individual processes contributing to the total transport are not stable. Here we investigate the MHT and its main components especially in the atmosphere, in five coupled climate model simulations from the Deep‐Time Model Intercomparison Project (DeepMIP). These simulations target the early Eocene climatic optimum, a geological time period with high CO2 concentrations, analog to the upper range of end‐of‐century CO2 projections. Preindustrial and early Eocene simulations, at a range of CO2 levels are used to quantify the MHT changes in response to both CO2 and non‐CO2 related forcings. We found that atmospheric poleward heat transport increases with CO2, while oceanic poleward heat transport decreases. The non‐CO2 boundary conditions cause more MHT toward the South Pole, mainly through an increase in the southward oceanic heat transport. The changes in paleogeography increase the heat transport via transient eddies at the northern mid‐latitudes in the Eocene. The Eocene Hadley cells do not transport more heat poleward, but due to the warmer atmosphere, especially the northern cell, circulate more heat in the tropics, than today. The monsoon systems' poleward latent heat transport increases with rising CO2 concentrations, but this change is counterweighted by the globally smaller Eocene monsoon area. Our results show that the changes in the monsoon systems' latent heat transport is a robust feature of CO2 warming, which is in line with the currently observed precipitation increase of present day monsoon systems.

Response of ocean climate to different heat-flux perturbations over the North Atlantic in FAFMIP

The diversity of surface-flux perturbations, especially for heat-flux perturbations, notably leads to uncertainties surrounding the responses of ocean climate under global warming scenarios projected by climate/Earth system models. However, when imposing heat-flux perturbations on the models, strong feedback persists between the atmosphere and the ocean, resulting in nearly doubled heat-flux perturbation over the North Atlantic (NA). Herein, quantitative evaluation of the influences of magnitude change of heat-flux perturbations over the NA on the changes in the Atlantic Meridional Overturning Circulation (AMOC), ocean heat uptake (OHU) and dynamic sea level (DSL) has been conducted by analysis of eight coupled model responses to the heat-flux perturbation experiments in the Flux-Anomaly-Forced Model Inter-comparison Project. It has been demonstrated that the magnitude of the AMOC change is extremely sensitive to the magnitude change of imposed NA heat-flux perturbation, and the weakening amplitude of the AMOC is nearly halved as the imposed heat-flux perturbation F is halved over the NA. The most remarkable responses of both DSL and OHU to the magnitude changes of NA heat-flux perturbation have been primarily found in the Atlantic and Arctic basins, especially for the NA region. Both the added ocean heat uptake (OHUa) and redistributed ocean heat uptake (OHUr) play key roles in OHU changes among the various NA heat-flux perturbation experiments. The magnitude change of NA-mean OHUa is almost linearly related to the imposed NA heat-flux perturbation, while the magnitude change of NA-mean OHUr, which is primarily caused by AMOC change and redistributed heat flux, is not proportional to the imposed NA heat-flux perturbation. There is a nearly linear relationship between the magnitude of AMOC change and the OHUr in tropical regions, including the regions in the low-latitude South Atlantic, the tropical Pacific Ocean and the Indian Ocean.

Evaluation of CMIP6 HighResMIP for Hydrologic Modeling of Annual Maximum Discharge in Iowa

AbstractThe High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.

Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals

AbstractThe magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime.

Contrasting Responses of Surface Heat Fluxes to Tropical Deforestation

AbstractDeforestation alters the exchange of heat, moisture, and momentum between the Earth's surface and the atmosphere, which can significantly affect the surface energy balance and water budget. However, changes in surface heat fluxes in response to deforestation are diverse among multi‐model simulations. This study explores factors that might cause different changes in surface heat fluxes due to tropical deforestation with NCAR Community Land Model. The results show that the changes in surface heat fluxes are related to the mean‐state flux partitioning conditions over the deforested areas. Deforestation tends to decrease both surface heat fluxes under conditions with smaller evaporative fractions (EFs), and tends to decrease the latent heat fluxes and increase the sensible heat fluxes under conditions with greater EFs. A similar relation can be found in the Land Use Model Intercomparison Project, which indicates that the varying simulated flux partitioning conditions might contribute to the diverse changes in surface heat fluxes among models.

Global and Zonal‐Mean Hydrological Response to Early Eocene Warmth

AbstractEarth's hydrological cycle is expected to intensify in response to global warming, with a “wet‐gets‐wetter, dry‐gets‐drier” response anticipated over the ocean. Subtropical regions (∼15°–30°N/S) are predicted to become drier, yet proxy evidence from past warm climates suggests these regions may be characterized by wetter conditions. Here we use an integrated data‐modeling approach to reconstruct global and zonal‐mean rainfall patterns during the early Eocene (∼56–48 million years ago). The Deep‐Time Model Intercomparison Project (DeepMIP) model ensemble indicates that the mid‐ (30°–60°N/S) and high‐latitudes (>60°N/S) are characterized by a thermodynamically dominated hydrological response to warming and overall wetter conditions. The tropical band (0°–15°N/S) is also characterized by wetter conditions, with several DeepMIP models simulating narrowing of the Inter‐Tropical Convergence Zone. However, the latter is not evident from the proxy data. The subtropics are characterized by negative precipitation‐evaporation anomalies (i.e., drier conditions) in the DeepMIP models, but there is surprisingly large inter‐model variability in mean annual precipitation (MAP). Intriguingly, we find that models with weaker meridional temperature gradients (e.g., CESM, GFDL) are characterized by a reduction in subtropical moisture divergence, leading to an increase in MAP. These model simulations agree more closely with our new proxy‐derived precipitation reconstructions and other key climate metrics and imply that the early Eocene was characterized by reduced subtropical moisture divergence. If the meridional temperature gradient was even weaker than suggested by those DeepMIP models, circulation‐induced changes may have outcompeted thermodynamic changes, leading to wetter subtropics. This highlights the importance of accurately reconstructing zonal temperature gradients when reconstructing past rainfall patterns.

Improved Performance of High‐Resolution Climate Models in Simulating Asian Monsoon Rainfall Extremes

AbstractRainfall extremes are one of the most difficult features to simulate using global climate models. Using pairs of high‐ and low‐resolution atmosphere‐only simulations from the High‐Resolution Model Intercomparison Project, we assess whether and how the horizontal grid spacing influences model's skill in simulating the Asian summer monsoon rainfall extremes. Despite the large inter‐model spread, improved model performance is seen across most models as resolution increases, both in terms of spatial distribution and intensity of monsoon rainfall extremes. Rainfall extremes are enhanced at the windward side of steep topography such as the foothills of Himalayas, and in the heavy monsoon regions such as central India, southern China, and western North Pacific. The improvements are found to be associated with a better representation of the vertical ascents and models' ability in simulating the rain‐producing synoptic low‐pressure systems, which are more frequent and stronger at higher resolutions.

National CO2budgets (2015–2020) inferred from atmospheric CO2observations in support of the global stocktake

Abstract. Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

Across-model spread and shrinking in predicting peatland carbon dynamics under global change.

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2 ) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Does Increasing Climate Model Horizontal Resolution Be Beneficial for the Mediterranean Region?: Multimodel Evaluation Framework for High‐Resolution Model Intercomparison Project

AbstractThe Mediterranean region (MR) is one of the climate change hot spots, posing serious threats to society. The insufficient resolution of the global climate models limits their capability to resolve the complex topography over MR, resulting in a large bias in climate variables. This study examines the performance of four Coupled Model Intercomparison Project phase six models from the High‐Resolution Model Intercomparison Project (HighResMIP) in reproducing the Mediterranean climate. A special focus is put on the role of oceanic and atmospheric model horizontal resolution and spread among the models. Various aspects, relevant to air‐sea interactions and precipitation are examined, including the mean, extremes, and associated mechanisms. All HighResMIP models reasonably well capture the large‐scale oceanic and atmospheric characteristics, but there is a sizable model‐to‐model difference. The Hadley Centre Global Environment Model and European Centre for Medium‐Range Weather Forecasts generally perform better than the other models at comparable atmospheric and oceanic horizontal resolutions. A finer oceanic resolution improves the representation not only of oceanic characteristics (i.e., sea surface temperature, SST) but also of the atmospheric processes (wind and precipitation). Likewise, increasing atmospheric model resolution benefits various atmospheric and ocean characteristics. Over some sub‐basins of the Mediterranean Sea, the intensification of surface wind speed results in the deepening of the ocean's mixed layer leading to cooling, indicating negative feedback in Wind‐SST. In contrast, in other regions, the warmer SST results in the intensification of wind (positive feedback in Wind‐SST).

Quantifying the ecological consequences of climate change in coastal ecosystems

Abstract Few coastal ecosystems remain untouched by direct human activities, and none are unimpacted by anthropogenic climate change. These drivers interact with and exacerbate each other in complex ways, yielding a mosaic of ecological consequences that range from adaptive responses, such as geographic range shifts and changes in phenology, to severe impacts, such as mass mortalities, ecological regime shifts and loss of biodiversity. Identifying the role of climate change in these phenomena requires corroborating evidence from multiple lines of evidence, including laboratory experiments, field observations, numerical models and palaeorecords. Yet few studies can confidently quantify the magnitude of the effect attributable solely to climate change, because climate change seldom acts alone in coastal ecosystems. Projections of future risk are further complicated by scenario uncertainty – that is, our lack of knowledge about the degree to which humanity will mitigate greenhouse-gas emissions, or will make changes to the other ways we impact coastal ecosystems. Irrespective, ocean warming would be impossible to reverse before the end of the century, and sea levels are likely to continue to rise for centuries and remain elevated for millennia. Therefore, future risks to coastal ecosystems from climate change are projected to mirror the impacts already observed, with severity escalating with cumulative emissions. Promising avenues for progress beyond such qualitative assessments include collaborative modelling initiatives, such as model intercomparison projects, and the use of a broader range of knowledge systems. But we can reduce risks to coastal ecosystems by rapidly reducing emissions of greenhouse gases, by restoring damaged habitats, by regulating non-climate stressors using climate-smart conservation actions, and by implementing inclusive coastal-zone management approaches, especially those involving nature-based solutions.

The influence of partial deforestation on moisture regime: sums of precipitation and total soil moisture content for the territory of Ukraine based on data of numerical experiment LUMIP

The paper is dedicated to the impact of partial deforestation on sums of precipitation and part/fraction or sums of precipitation intercepted by the canopy; total soil moisture content under condition of minimal anthropogenic influence for the territory of Ukraine. In this work used data from the Land Use Model Intercomparison Project (LUMIP), which is based on Global Climate Models (GCMs). The purpose of LUMIP is to reveal patterns of changes of climate characteristics due to gradual global deforestation with a trend approximately 1 % every year and its replacement on grass or pastures for period 1850—1899. This time span is considered as the pre-industrial. The next period, 1900—1929 is without changes in forest cover. For estimation of impacts of partial deforestation the methods of anomalies over the first 20-year period (1850—1869) was used with further application of 5-year running mean of climatic characteristics to smooth their interannual variability. Coefficients of linear correlation between running mean anomalies of specific characteristic and the percentage of forest cover in grid cells with averaging over latitudinal zones, linear trends and differences were estimated.
 Overally estimations have shown that the global partial deforestation doesn’t impact directly on of monthly total precipitation in the middle latitudes, particularly, in Ukraine. But gradual forest reduction decreases part or fraction of precipitation intercepted by the canopy in both cold and warm seasons with average trend up to –2,6....–1,5 %/10 years in latitudinal zones in specific grids, where forest cover was reduced. The maximal decline was up to –14.5 % in grids as the difference between the 50-year period of partial deforestation and the next 30-year period. Such an effect will causemore precipitation reaching the ground and most likely will influence surface runoff. Found, that it causes the increase in total soil moisture content with trend 0.1—4.9 %/10 years depending on month and climate model, but with most significant changes during summer and autumn seasons.
 Thus, the analysis of LUMIP data for Ukraine has revealed that the partial deforestation caused the decrease of fraction part/fraction of precipitation intercepted by the canopy. Most GCMs demonstrated that this reduction provoked the increase of total soil moisture content and also can cause changes in hydrological regime and provoke higher flood frequency and other related hydrometeorological hazards in the forest part of Ukraine.

Evaluating Global Atmospheric Inversions of Terrestrial Net Ecosystem Exchange CO2 Over North America on Seasonal and Sub‐Continental Scales

AbstractAtmospheric inversion estimates of net ecosystem exchange (NEE) of CO2 are increasingly relevant to climate policy. We evaluated sub‐continental, seasonal estimates of CO2 NEE from nine global inversion systems that participated in the Orbiting Carbon Observatory‐2 model intercomparison project (OCO‐2 v9 MIP), using 98 research flights conducted over the central and eastern United States from 2016 to 2018 as part of the Atmospheric Carbon and Transport ‐ America mission. We found that the seasonal amplitude of NEE in the central and eastern United States is underestimated in these models and model‐data biases are largest for those inversions with the smallest seasonal flux amplitudes. These results were independent of whether the inversions used satellite or in situ data. The largest NEE biases were observed in the Midwest croplands and eastern forests. Future experiments are needed to determine the causes of the persistent biases and if they are associated with biases in annual flux estimates.

From Many to One: Consensus Inference in a MIP

AbstractA Model Intercomparison Project (MIP) consists of teams who estimate the same underlying quantity (e.g., temperature projections to the year 2070). A simple average of the ensemble of the teams' outputs gives a consensus estimate, but it does not recognize that some outputs are more variable than others. Statistical analysis of variance (ANOVA) models offer a way to obtain a weighted frequentist consensus estimate of outputs with a variance that is the smallest possible. Modulo dependence between MIP outputs, the ANOVA approach weights a team's output inversely proportional to its variance, from which optimally weighted estimates follow. ANOVA weights can also provide a prior distribution for Bayesian Model Averaging of the MIP outputs when external evaluation data are available. We use a MIP of carbon‐dioxide‐flux inversions to illustrate the ANOVA‐based weighting and subsequent frequentist consensus inferences.

Impacts of Different Land Use Scenarios on Future Global and Regional Climate Extremes

Land use and land cover change (LULCC) alters the character of the land surface and directly impacts the climate. The impacts of LULCC on historical and future climate have been largely investigated, mostly using simulations with or without land use change. However, it is still not clear to what extent the projections of future climate change depend on the choice of land use scenario, which can provide important guidance on using land use and land management as a tool for regional climate mitigation. Here, using ten Earth system models participating in future land use policy sensitivity experiments in Land Use Model Intercomparison Project (LUMIP), we assessed the impact of two different land use scenarios (SSP1-2.6 and SSP3-7.0) on extreme climate. The results demonstrate that the use of different land use change scenarios has a substantial effect on the projections of regional climate extreme changes. Our study also reveals that, compared with other anthropogenic forcings, land use change has a considerable contribution to regional temperature extreme changes, with the contribution ranging from −14.0% to 10.3%, and the contribution to regional precipitation extreme change is larger, with a range of −118.4~138.8%. The global climate effects of land use change are smaller in magnitude than regional effects, with a small (5%) contribution to temperature extreme change. We also found a large spread in the model’s responses to LULCC, especially for precipitation extremes, suggesting that observation-based studies on reducing models’ uncertainties are needed to obtain more robust future projections of regional climate change. Our study highlights the essential role of land use and land management strategies in future regional climate mitigation.

African Hydroclimate During the Early Eocene From the DeepMIP Simulations.

The early Eocene (∼56–48 Myr ago) is characterized by high CO2 estimates (1,200–2,500 ppmv) and elevated global temperatures (∼10°C–16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e.g., Africa). Here, we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state‐of‐the‐art climate models in the Deep‐time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre‐industrial simulations and modern observations suggests that model biases are model‐ and geographically dependent, however, these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre‐industrial suggests that there is no obvious wetting or drying trend as the CO2 increases. The results suggest that changes to the land sea mask (relative to modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa. There is an increase in precipitation over equatorial and West Africa and associated drying over northern Africa as CO2 rises. There are also important dynamical changes, with evidence that anticyclonic low‐level circulation is replaced by increased south‐westerly flow at high CO2 levels. Lastly, a model‐data comparison using newly compiled quantitative climate estimates from paleobotanical proxy data suggests a marginally better fit with the reconstructions at lower levels of CO2.

Early Eocene Ocean Meridional Overturning Circulation: The Roles of Atmospheric Forcing and Strait Geometry

AbstractHere, we compare the ocean overturning circulation of the early Eocene (47–56 Ma) in eight coupled climate model simulations from the Deep‐Time Model Intercomparison Project (DeepMIP) and investigate the causes of the observed inter‐model spread. The most common global meridional overturning circulation (MOC) feature of these simulations is the anticlockwise bottom cell, fed by sinking in the Southern Ocean. In the North Pacific, one model (GFDL) displays strong deepwater formation and one model (CESM) shows weak deepwater formation, while in the Atlantic two models show signs of weak intermediate water formation (MIROC and NorESM). The location of the Southern Ocean deepwater formation sites varies among models and relates to small differences in model geometry of the Southern Ocean gateways. Globally, convection occurs in the basins with smallest local freshwater gain from the atmosphere. The global MOC is insensitive to atmospheric CO2 concentrations from 1× (i.e., 280 ppm) to 3× (840 ppm) pre‐industrial levels. Only two models have simulations with higher CO2 (i.e., CESM and GFDL) and these show divergent responses, with a collapsed and active MOC, respectively, possibly due to differences in spin‐up conditions. Combining the multiple model results with available proxy data on abyssal ocean circulation highlights that strong Southern Hemisphere‐driven overturning is the most likely feature of the early Eocene. In the North Atlantic, unlike the present day, neither model results nor proxy data suggest deepwater formation in the open ocean during the early Eocene, while the evidence for deepwater formation in the North Pacific remains inconclusive.

Impact of deforestation on moisture evaporation from soil and canopy for the territory of Ukraine based on data of numerical experiment LUMIP

The study presents results of analysis of the impact of partial deforestation on spatio-temporal distribution of the outflow part of water balance, namely evaporation from soil and canopy. The data of 6 Global climate models of theoretical experiment Land Use Model Intercomparison Project (LUMIP) was used in the research. The aim of this experiment is to reveal the influence of global deforestation with further replacement by grass cover on distribution of climate characteristics. It was done for the period 1850—1929, where the first part 1850—1899 refers to the pre-industrial period or period with minimal mostly constant anthropogenic influence; the second part is the next 30 years — 1900—1929. During the pre-industrial period 1850—1899 land cover was reduced globally with a trend of 400 thousand km2 per year and during 1900—1929 it was stable. Defining the impact of deforestation, the normalization over the first 20 years (1850—1869) was performed and there were found anomalies of climatic characteristics as difference to this basic period. Deforestation with further replacement of the forest cover by grass causes an increase in soil evaporation with the trend up to 1.6 mm/10 years in the warm season with more significant changes in April —July, as deforestation reveals more of the soil. Thus, the correlation was −0.8 ...−0.4 between forest cover and soil evaporation with maximal changes in April. It can be connected with grass being sparse in this period but later on covering more soil and preventing intensive evaporation. On the contrary, evaporation from canopy in global climate models is reduced with deforestation as the evaporation area is shrunk. This effect was revealed during all seasons in most grid points, where deforestation occurs. But the biggest change is found in spring and summer months with values up to −0.8 mm/10 years and correlation r = 0.4 ... 0.9 depending on the model and the season. Thus, we found an effect of increasing evaporation from soil while decreasing evaporation from canopy in climate modeling as the effect of partial deforestation on the territory of Ukraine. These changes can cause redistribution in water balance components of the territory and have consequences for hydrological regime, agrometeorology etc. In particular, the increase in soil evaporation due to deforestation can provoke more intensive soil aridization and degradation. The influence of deforestation on total soil moisture and regime of precipitation will be presented in the next publication.