The purpose of this paper is to give a rather comprehensive description of the models for natural and anthropogenically driven changes in biogeography as implemented in the land component JSBACH of ...the Max Planck Institute Earth system model (MPI‐ESM). The model for natural land cover change (DYNVEG) features two types of competition: between the classes of grasses and woody types (trees, shrubs) controlled by disturbances (fire, windthrow) and within those vegetation classes between different plant functional types based on relative net primary productivity advantages. As part of this model, the distribution of land unhospitable to vegetation (hot and cold deserts) is determined dynamically from plant productivity under the prevailing climate conditions. The model for anthropogenic land cover change implements the land use transition approach by Hurtt et al. (2006). Our implementation is based on the assumption that historically pastures have been preferentially established on former grasslands (“pasture rule”). We demonstrate that due to the pasture rule, deforestation reduces global forest area between 1850 and 2005 by 15% less than without. Because of the pasture rule the land cover distribution depends on the full history of land use transitions. This has implications for the dynamics of natural land cover change because assumptions must be made on how agriculturalists react to a changing natural vegetation in their environment. A separate model representing this process has been developed so that natural and anthropogenic land cover change can be simulated consistently. Certain aspects of our model implementation are illustrated by selected results from the recent CMIP5 simulations.
Key Points
Description of models for natural and anthropogenic land cover change
Accounting for the preferential allocation of pastures on former grasslands
Accounting for land use change induced by changes in natural vegetation
The effects of land-use changes on climate are assessed using specified-concentration simulations complementary to the representative concentration pathway 2.6 (RCP2.6) and RCP8.5 scenarios performed ...for phase 5 of the Coupled Model Intercomparison Project (CMIP5). This analysis focuses on differences in climate and land–atmosphere fluxes between the ensemble averages of simulations with and without land-use changes by the end of the twenty-first century. Even though common land-use scenarios are used, the areas of crops and pastures are specific for each Earth system model (ESM). This is due to different interpretations of land-use classes. The analysis reveals that fossil fuel forcing dominates land-use forcing. In addition, the effects of land-use changes are globally not significant, whereas they are significant for regions with land-use changes exceeding 10%. For these regions, three out of six participating models—the Second Generation Canadian Earth System Model (CanESM2); Hadley Centre Global Environmental Model, version 2 (Earth System) (HadGEM2-ES); and Model for Interdisciplinary Research on Climate, Earth System Model (MIROC-ESM)—reveal statistically significant changes in mean annual surface air temperature. In addition, changes in land surface albedo, available energy, and latent heat fluxes are small but significant for most ESMs in regions affected by land-use changes. These climatic effects are relatively small, as land-use changes in the RCP2.6 and RCP8.5 scenarios are small in magnitude and mainly limited to tropical and subtropical regions. The relative importance of the climatic effects of land-use changes is higher for the RCP2.6 scenario, which considers an expansion of biofuel croplands as a climate mitigation option. The underlying similarity among all models is the loss in global land carbon storage due to land-use changes.
The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID ...used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the individual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.
Seven climate models were used to explore the biogeophysical impacts of human‐induced land cover change (LCC) at regional and global scales. The imposed LCC led to statistically significant decreases ...in the northern hemisphere summer latent heat flux in three models, and increases in three models. Five models simulated statistically significant cooling in summer in near‐surface temperature over regions of LCC and one simulated warming. There were few significant changes in precipitation. Our results show no common remote impacts of LCC. The lack of consistency among the seven models was due to: 1) the implementation of LCC despite agreed maps of agricultural land, 2) the representation of crop phenology, 3) the parameterisation of albedo, and 4) the representation of evapotranspiration for different land cover types. This study highlights a dilemma: LCC is regionally significant, but it is not feasible to impose a common LCC across multiple models for the next IPCC assessment.
Afforestation and reforestation have become popular instruments of climate mitigation policy, as forests are known to store large quantities of carbon. However, they also modify the fluxes of energy, ...water and momentum at the land surface. Previous studies have shown that these biogeophysical effects can counteract the carbon drawdown and, in boreal latitudes, even overcompensate it due to large albedo differences between forest canopy and snow. This study investigates the role forest cover plays for global climate by conducting deforestation and afforestation experiments with the earth system model of the Max Planck Institute for Meteorology (MPI-ESM). Complete deforestation of the tropics (18.75° S–15° N) exerts a global warming of 0.4 °C due to an increase in CO2 concentration by initially 60 ppm and a decrease in evapotranspiration in the deforested areas. In the northern latitudes (45° N–90° N), complete deforestation exerts a global cooling of 0.25 °C after 100 years, while afforestation leads to an equally large warming, despite the counteracting changes in CO2 concentration. Earlier model studies are qualitatively confirmed by these findings. As the response of temperature as well as terrestrial carbon pools is not of equal sign at every land cell, considering forests as cooling in the tropics and warming in high latitudes seems to be true only for the spatial mean, but not on a local scale.
The ICON Earth System Model Version 1.0 Jungclaus, J. H.; Lorenz, S. J.; Schmidt, H. ...
Journal of advances in modeling earth systems,
April 2022, 2022-04-00, 20220401, 2022-04-01, Letnik:
14, Številka:
4
Journal Article
Recenzirano
Odprti dostop
This work documents the ICON‐Earth System Model (ICON‐ESM V1.0), the first coupled model based on the ICON (ICOsahedral Non‐hydrostatic) framework with its unstructured, icosahedral grid concept. The ...ICON‐A atmosphere uses a nonhydrostatic dynamical core and the ocean model ICON‐O builds on the same ICON infrastructure, but applies the Boussinesq and hydrostatic approximation and includes a sea‐ice model. The ICON‐Land module provides a new framework for the modeling of land processes and the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are represented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin‐up of a base‐line version at a resolution typical for models participating in the Coupled Model Intercomparison Project (CMIP). The performance of ICON‐ESM is assessed by means of a set of standard CMIP6 simulations. Achievements are well‐balanced top‐of‐atmosphere radiation, stable key climate quantities in the control simulation, and a good representation of the historical surface temperature evolution. The model has overall biases, which are comparable to those of other CMIP models, but ICON‐ESM performs less well than its predecessor, the Max Planck Institute Earth System Model. Problematic biases are diagnosed in ICON‐ESM in the vertical cloud distribution and the mean zonal wind field. In the ocean, sub‐surface temperature and salinity biases are of concern as is a too strong seasonal cycle of the sea‐ice cover in both hemispheres. ICON‐ESM V1.0 serves as a basis for further developments that will take advantage of ICON‐specific properties such as spatially varying resolution, and configurations at very high resolution.
Plain Language Summary
ICON‐ESM is a completely new coupled climate and earth system model that applies novel design principles and numerical techniques. The atmosphere model applies a non‐hydrostatic dynamical core, both atmosphere and ocean models apply unstructured meshes, and the model is adapted for high‐performance computing systems. This article describes how the component models for atmosphere, land, and ocean are coupled together and how we achieve a stable climate by setting certain tuning parameters and performing sensitivity experiments. We evaluate the performance of our new model by running a set of experiments under pre‐industrial and historical climate conditions as well as a set of idealized greenhouse‐gas‐increase experiments. These experiments were designed by the Coupled Model Intercomparison Project (CMIP) and allow us to compare the results to those from other CMIP models and the predecessor of our model, the Max Planck Institute for Meteorology Earth System Model. While we diagnose overall satisfactory performance, we find that ICON‐ESM features somewhat larger biases in several quantities compared to its predecessor at comparable grid resolution. We emphasize that the present configuration serves as a basis from where future development steps will open up new perspectives in earth system modeling.
Key Points
This work documents ICON‐ESM 1.0, the first version of a coupled model based on the ICON framework
Performance of ICON‐ESM is assessed by means of CMIP6 Diagnosis, Evaluation, and Characterization of Klima experiments at standard CMIP‐type resolution
ICON‐ESM reproduces the observed temperature evolution. Biases in clouds, winds, sea‐ice, and ocean properties are larger than in MPI‐ESM
Key Points
Two metrics are proposed to evaluate vegetation cover simulated by ESMs
On a global scale, tree cover is satisfactory simulated by MPI‐ESM
Land‐surface albedois evaluated using the net ...surface radiation
In recent generation Earth system models (ESMs), land‐surface grid cells are represented as tiles covered by different plant functional types such as trees or grasses. Here, we present an evaluation of the vegetation‐cover module of the ESM developed at the Max Planck Institute for Meteorology in Hamburg, Germany (MPI‐ESM) for present‐day conditions. The vegetation continuous fields (VCF) product that is based on satellite observations in 2001 is used to evaluate the fractional distributions of woody vegetation cover and bare ground. The model performance is quantified using two metrics: a square of the Pearson correlation coefficient, r2, and the root‐mean‐square error (RMSE). On a global scale, r2 and RMSE of modeled tree cover are equal to 0.61 and 0.19, respectively, which we consider as satisfactory values. The model simulates tree cover and bare ground with r2 higher for the Northern Hemisphere (0.66) than for the Southern Hemisphere (0.48–0.50). We complement this analysis with an evaluation of the simulated land‐surface albedo using the difference in net surface radiation. On a global scale, the correlation between modeled and observed albedos is high during all seasons, whereas the main disagreement occurs in spring in the high northern latitudes. This discrepancy can be attributed to a high sensitivity of the land‐surface albedo to the simulated snow cover and snow‐masking effect of trees. By contrast, the tropics are characterized by very high correlation and relatively low RMSE (5.4–6.5 W/m2) during all seasons. The presented approach could be applied for an evaluation of vegetation cover and land‐surface albedo simulated by different ESMs.
The factor separation of Stein and Alpert (1993) is applied to simulations with the MPI Earth system model to determine the factors which cause the differences between vegetation patterns in glacial ...and pre-industrial climate. The factors firstly include differences in the climate, caused by a strong increase in ice masses and the radiative effect of lower greenhouse gas concentrations; secondly, differences in the ecophysiological effect of lower glacial atmospheric CO2 concentrations; and thirdly, the synergy between the pure climate effect and the pure effect of changing physiologically available CO2 . It is has been shown that the synergy can be interpreted as a measure of the sensitivity of ecophysiological CO2 effect to climate. The pure climate effect mainly leads to a contraction or a shift in vegetation patterns when comparing simulated glacial and pre-industrial vegetation patterns. Raingreen shrubs benefit from the colder and drier climate. The pure ecophysiological effect of CO2 appears to be stronger than the pure climate effect for many plant functional types - in line with previous simulations. The pure ecophysiological effect of lower CO2 mainly yields a reduction in fractional coverage, a thinning of vegetation and a strong reduction in net primary production. The synergy appears to be as strong as each of the pure contributions locally, but weak on global average for most plant functional types. For tropical evergreen trees, however, the synergy is strong on global average. It diminishes the difference between glacial and pre-industrial coverage of tropical evergreen trees, due to the pure climate effect and the pure ecophysiological CO2 effect, by approximately 50 per cent.
Biogeophysical (BGP) and biogeochemical (BGC) effects of land-use and land cover change (LULCC) are separated at the global and regional scales in new interactive CO2 simulations for the 21st ...century. Results from four earth system models (ESMs) are analyzed for the future RCP8.5 scenario from simulations with and without land-use and land cover change (LULCC), contributing to the Land-Use and Climate, IDentification of robust impacts (LUCID) project. Over the period 2006–2100, LULCC causes the atmospheric CO2 concentration to increase by 12, 22, and 66 ppm in CanESM2, MIROC-ESM, and MPI-ESM-LR, respectively. Statistically significant changes in global near-surface temperature are found in three models with a BGC-induced global mean annual warming between 0.07 and 0.23 K. BGP-induced responses are simulated by three models in areas of intense LULCC of varying sign and magnitude (between −0.47 and 0.10 K). Modifications of the land carbon pool by LULCC are disentangled in accordance with processes that can lead to increases and decreases in this carbon pool. Global land carbon losses due to LULCC are simulated by all models: 218, 57, 35 and 34 Gt C by MPI-ESM-LR, MIROC-ESM, IPSL-CM5A-LR and CanESM2, respectively. On the contrary, the CO2-fertilization effect caused by elevated atmospheric CO2 concentrations due to LULCC leads to a land carbon gain of 39 Gt C in MPI-ESM-LR and is almost negligible in the other models. A substantial part of the spread in models' responses to LULCC is attributed to the differences in implementation of LULCC (e.g., whether pastures or crops are simulated explicitly) and the simulation of specific processes. Simple idealized experiments with clear protocols for implementing LULCC in ESMs are needed to increase the understanding of model responses and the statistical significance of results, especially when analyzing the regional-scale impacts of LULCC.
PDF
