scholarly journals Climate-induced hysteresis of the tropical forest in a fire-enabled Earth system model

2021 ◽  
Author(s):  
Markus Drüke ◽  
Werner von Bloh ◽  
Boris Sakschewski ◽  
Nico Wunderling ◽  
Stefan Petri ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Earth System Model ◽  
Forest Recovery ◽  
System Model ◽  
Earth System ◽  
Dynamic Global Vegetation Model ◽  
Original State ◽  
Impact Phase ◽  
The Impact

AbstractTropical rainforests are recognized as one of the terrestrial tipping elements which could have profound impacts on the global climate, once their vegetation has transitioned into savanna or grassland states. While several studies investigated the savannization of, e.g., the Amazon rainforest, few studies considered the influence of fire. Fire is expected to potentially shift the savanna-forest boundary and hence impact the dynamical equilibrium between these two possible vegetation states under changing climate. To investigate the climate-induced hysteresis in pan-tropical forests and the impact of fire under future climate conditions, we employed the Earth system model CM2Mc, which is biophysically coupled to the fire-enabled state-of-the-art dynamic global vegetation model LPJmL. We conducted several simulation experiments where atmospheric CO$$_2$$ 2 concentrations increased (impact phase) and decreased from the new state (recovery phase), each with and without enabling wildfires. We find a hysteresis of the biomass and vegetation cover in tropical forest systems, with a strong regional heterogeneity. After biomass loss along increasing atmospheric CO$$_2$$ 2 concentrations and accompanied mean surface temperature increase of about 4 $$^\circ $$ ∘ C (impact phase), the system does not recover completely into its original state on its return path, even though atmospheric CO$$_2$$ 2 concentrations return to their original state. While not detecting large-scale tipping points, our results show a climate-induced hysteresis in tropical forest and lagged responses in forest recovery after the climate has returned to its original state. Wildfires slightly widen the climate-induced hysteresis in tropical forests and lead to a lagged response in forest recovery by ca. 30 years.

Climate-induced hysteresis of the tropical forest in the fire-enabled Earth system model CM2Mc-LPJmL

2021 ◽  
Author(s):  
Markus Drüke ◽  
Werner v. Bloh ◽  
Boris Sakschewski ◽  
Nico Wunderling ◽  
Stefan Petri ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Atmospheric Co2 ◽  
Forest Recovery ◽  
Surface Model ◽  
Original State ◽  
Co2 Concentrations ◽  
Impact Phase ◽  
Atmospheric Co2 Concentrations ◽  
The Impact

<p>Tropical rainforests are recognized as one of the terrestrial tipping elements which could have profound impacts on the global climate, once their vegetation has transitioned into savanna or grassland states. While several studies investigated the savannization of, e.g., the Amazon rainforest, few studies considered the influence of fire. Fire is expected to potentially shift the savanna-forest boundary and hence impact the dynamical equilibrium between these two possible vegetation states under changing climate. To investigate the climate-induced hysteresis in pan-tropical forests and the impact of fire under future climate conditions, we coupled the well established and comprehensively validated Dynamic Global Vegetation Model LPJmL5.0-FMS to the coupled climate model CM2Mc, which is based on the atmosphere model AM2 and the ocean model MOM5 (CM2Mc-LPJmL v1.0). In CM2Mc, we replaced the simple land surface model LaD with LPJmL and fully coupled the water and energy cycles. Exchanging LaD by LPJmL, and therefore switching from a static and prescribed vegetation to a dynamic vegetation, allows us to model important biosphere processes, including wildfire, tree mortality, permafrost, hydrological cycling, and the impacts of managed land (crop growth and irrigation).</p><p>With CM2Mc-LPJmL we conducted simulation experiments where atmospheric CO2 concentrations increased from a pre-industrial level up to 1280 ppm (impact phase) followed by a recovery phase where CO2 concentrations reach pre-industrial levels again. This experiment is performed with and without allowing for wildfires. We find a hysteresis of the biomass and vegetation cover in tropical forest systems, with a strong regional heterogeneity. After biomass loss along increasing atmospheric CO2 concentrations and accompanied mean surface temperature increase of about 4°C (impact phase), the system does not recover completely into its original state on its return path, even though atmospheric CO2 concentrations return to their original state. While not detecting large-scale tipping points, our results show a climate-induced hysteresis in tropical forest and lagged responses in forest recovery after the climate has returned to its original state. Wildfires slightly widen the climate-induced hysteresis in tropical forests and lead to a lagged response in forest recovery by ca. 30 years.</p>

Coupling the dynamic vegetation model LPJmL5.1 to an Earth system model – towards POEM1.0

2020 ◽  
Author(s):  
Markus Drüke ◽  
Werner von Bloh ◽  
Stefan Petri ◽  
Sibyll Schaphoff ◽  
Boris Sakschewski ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Model Performance ◽  
Coupled Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Vegetation Model ◽  
Canopy Layer ◽  
Dynamic Global Vegetation Model ◽  
The Impact

<p>Feedbacks between biosphere and other components of the Earth system are challenging to model accurately and therefore are often omitted or oversimplified in Earth system models (ESMs). However, their importance is increasingly recognized as rapid disturbances due to anthropogenic (e.g., deforestation) or natural (e.g. regional increase in fires) drivers are already observed.</p><p>Here we couple the well established and comprehensively validated dynamic global vegetation model LPJmL5.1 (von Bloh et al., 2018) to an Earth System model CM2Mc (MOM5/AM2, Galbraith et al. 2011). We replace the simple static vegetation model LaD with LPJmL5.1 and couple the water- and energy cycle by using GFDL’s Flexible Modeling System (FMS). In order to stabilize the model performance, several adjustments to LPJmL5.1 had to be done, including the introduction of a subdaily cycle for the energy and water calculations, the implementation of a conductance of the soil evaporation and plant interception, the calculation of a canopy layer humidity, and the surface energy balance in order to calculate the surface and canopy layer temperature within LPJmL5.1.</p><p>The coupled system allows us to answer questions regarding ecosystem stability with a complete energy and water cycle. For example, changes in the vegetation have a large impact on atmosphere dynamics, which in turn affects precipitation and feeds back into vegetation growth and mortality. To examine this feedback a simple experiment is performed by deforesting the whole Amazon basin and replacing it with grassland. Our results show decreasing precipitation and increasing canopy temperature which becomes a stable climate state in this treeless scenario. Future applications of the coupled model may include the investigation of tipping points in the biosphere, the impact of different atmospheric CO<sub>2</sub> concentrations or climate change and land-use change scenarios.</p><p> </p>

scholarly journals Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2

2019 ◽  
Vol 12 (7) ◽  
pp. 3099-3118 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
Dave MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Southern Oscillation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

scholarly journals Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

2019 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
David MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Mean ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

Ocean carbon cycle during the last deglaciation in the Max Planck Institute Earth System Model

2020 ◽  
Author(s):  
Bo Liu ◽  
Katharina Six ◽  
Tatiana Ilyina
Keyword(s):  
Organic Matter ◽  
Marine Sediment ◽  
Water Discharge ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Dust Deposition ◽  
Max Planck ◽  
Terrestrial Organic Matter ◽  
The Impact

<p>The deglacial atmospheric CO2 increase has been attributed to a combination of mechanisms, many of which relate to the ocean outgassing triggered by changing marine physical and biogeochemical states. To quantify the impact of proposed processes and feedback on the deglacial CO2 rise, previous modelling studies mostly conducted time-slice sensitivity experiments. Here, we present results from a transient deglaciation simulation (24 kB.P. - 1850) using the comprehensive Max Planck Institute Earth System Model (MPI-ESM). We force the model with the deglacial atmospheric greenhouse gases (CO2, CH4, N2O) concentrations, obital parameters, ice sheet reconstruction and transient dust deposition. The ocean biogeochemical component of MPI-ESM is using the same automatical adjustment of bathymetry and land-sea mask in response to deglacial continental runoff and melt water discharge. In and around the areas of changing land-sea mask, we redistribute the marine biogeochemical tracers in accord with the simulated salinity. Terrestrial organic matter is transferred from flooded land areas to the ocean, which guarantees mass conservation with respect to carbon. We also include 13C tracers in the ocean biogeochemical component to evaluate the simulated ocean state against proxy data. The initial marine nutrients and carbon inventories are set the same as those in the present-day ocean. <br>During the first 3 kyr, the climate and ocean state show, as expected, only modest variations. Some flooding events of coastal areas bring terrestrial organic matter to the ocean and lead locally to CO2 outgassing for several decades. Terrestrial organic matter has a higher carbon to nutrient stoichiometry as compared to marine organic matter, thus its remineralization favours CO2 outgassing. Additionally, the accumulation of terrestrial organic matter in the top layers of the marine sediment reduces the replenishment of the water-column nutrients by the re-flux of remineralization products from marine sediment. Consequently, the strength of the local biological pump decreases. Further results will be presented and discussed. </p>

scholarly journals Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

2017 ◽  
Vol 11 (6) ◽  
pp. 2919-2942 ◽  
Author(s):  
Petri Räisänen ◽  
Risto Makkonen ◽  
Alf Kirkevåg ◽  
Jens B. Debernard
Keyword(s):  
Grain Size ◽  
Sea Ice ◽  
Grain Shape ◽  
Earth System Model ◽  
System Model ◽  
Blowing Snow ◽  
Earth System ◽  
Spherical Case ◽  
The Impact ◽  
Global Mean

Abstract. Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region) than in the spherical case ( ≈  0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.

scholarly journals Introducing the new Regional Community Earth System Model, R-CESM

2021 ◽  
pp. 1-53
Author(s):  
Dan Fu ◽  
Justin Small ◽  
Jaison Kurian ◽  
Yun Liu ◽  
Brian Kauffman ◽  
...  
Keyword(s):  
High Resolution ◽  
Earth System Model ◽  
Model Systems ◽  
System Model ◽  
Earth System ◽  
Regional Ocean Modeling System ◽  
Ocean Atmosphere ◽  
Community Earth System Model ◽  
Regional Community ◽  
The Impact

AbstractThe development of high-resolution, fully-coupled, regional Earth system model systems is important for improving our understanding of climate variability, future projections, and extreme events at regional scales. Here we introduce and present an overview of the newly-developed Regional Community Earth System Model (R-CESM). Different from other existing regional climate models, R-CESM is based on the Community Earth System Model version 2 (CESM2) framework. We have incorporated the Weather Research and Forecasting (WRF) model and Regional Ocean Modeling System (ROMS) into CESM2 as additional components. As such, R-CESM can be conveniently used as a regional dynamical downscaling tool for the global CESM solutions or/and as a standalone high-resolution regional coupled model. The user interface of R-CESM follows that of CESM, making it readily accessible to the broader community. Among countless potential applications of R-CESM, we showcase here a few preliminary studies that illustrate its novel aspects and value. These include: 1) assessing the skill of R-CESM in a multi-year, high-resolution, regional coupled simulation of the Gulf of Mexico; 2) examining the impact of WRF and CESM ocean-atmosphere coupling physics on tropical cyclone simulations; and 3) a convection-permitting simulation of submesoscale ocean-atmosphere interactions. We also discuss capabilities under development such as i) regional refinement using a high-resolution ROMS nested within global CESM; and ii) “online” coupled data assimilation. Our open-source framework (publicly available at https://github.com/ihesp/rcesm1) can be easily adapted to a broad range of applications that are of interest to the users of CESM, WRF, and ROMS.

scholarly journals Surface mass balance downscaling through elevation classes in an Earth System Model: analysis, evaluation and impacts on the simulated climate

2019 ◽  
Author(s):  
Raymond Sellevold ◽  
Leonardus van Kampenhout ◽  
Jan T. M. Lenaerts ◽  
Brice Noël ◽  
William H. Lipscomb ◽  
...  
Keyword(s):  
Mass Balance ◽  
Dynamical Downscaling ◽  
Greenland Ice Sheet ◽  
Earth System Model ◽  
System Model ◽  
Lapse Rate ◽  
Surface Mass Balance ◽  
Earth System ◽  
Surface Mass ◽  
The Impact

Abstract. The modeling of ice sheets in Earth System Models (ESMs) is an active area of research with applications to future sea level rise projections and paleoclimate studies. A major challenge for the surface mass balance (SMB) modeling with ESMs arises from their coarse resolution. This paper evaluates the elevation classes (EC) method as an SMB downscaling alternative to the dynamical downscaling of regional climate models. To this end, we compare EC-simulated elevation dependent surface energy and mass balance gradients from the Community Earth System Model 1.0 (CESM1.0) with those from RACMO2.3. The EC implementation in CESM1.0 combines prognostic snow albedo, a multi-layer snow model, and elevation corrections for two atmospheric forcing variables: temperature and humidity. Despite making no corrections for incoming radiation and precipitation, we find that the EC method in CESM1.0 yields similar SMB gradients as RACMO2.3, in part due to compensating biases in snowfall, surface melt and refreezing gradients. We discuss the sensitivity of the results to the lapse rate used for the temperature correction. We also evaluate the impact of the EC method on the climate simulated by the ESM and find minor cooling over the Greenland ice sheet and Barents and Greenland Seas, which corrects a warm bias in the ESM due to topographic smoothing. Based on our diagnostic procedure to evaluate the EC method, we make several recommendations for future implementations.

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