Philipp de Vrese

A robust proportionality between radiative forcing and global-mean temperature has been established for most climate drivers. However, land-surface changes are considered an exception due to their potential to alter surface hydrology, introducing non-radiative effects that may rival those from surface albedo changes. Here, comparing Earth-system-model simulations with large-scale perturbations of surface albedo and water availability, we find that the spatially integrated biophysical climate impact is similarly dominated by radiative processes and scales approximately linearly with the resulting shifts in planetary albedo, implying a ~ 0.4 K surface temperature response per W m ⁻² change in top-of-atmosphere net short-wave radiation. Thus, while non-radiative effects strongly influence local temperatures, their net contribution to the global-mean signal is negligible. The planetary-albedo response is primarily governed by regional cloud-cover characteristics, resulting in a substantially stronger temperature response to land-surface change in mid- and high-latitude regions than in the tropics and subtropics.

The vast majority of land-surface models uses a tiling-approach to capture the effects of subgrid-scale spatial heterogeneity in the land-surface properties. In most cases, however, the tiles, which represent patches with homogeneous characteristics at and below the surface, are treated independently of each other and the lateral exchange between them is not being taken into consideration. The present manuscript describes an approach for a tile-based representation of lateral exchange processes in heterogeneous landscapes that was recently implemented into ICON-Land, the land surface component of the ICON framework. The scheme captures the horizontal fluxes on a broad range of spatial scales and represents 5 lateral exchange processes, namely gravity-driven moisture fluxes and the corresponding advective heat transport, diffusive- and conductive fluxes of water and heat as well as the redistribution of snow between small-scale topographic features. In the approach, the relationships between any two tiles are determined by a set of characteristic connectivities which are treated as inherent properties of the pair of tiles – invariant in time and independent of the location – and derive from the internal logic underlying the definition of the tiles. The characteristic connectivities are used to calculate the spatio-geometric relationships between two tiles, such as the (geometrical) contact length or the characteristic center-to-center distance between two adjacent surface clusters. These, in turn, define gradients as well as the time-lag factors that govern the lateral transport in the model. In addition to a description of the model development, we present two example applications of the new scheme, which address the effect of sub-grid scale fluxes on the model's ability to capture the spatial variability in the state of the surface and sub-surface and the overall terrestrial water storage. Here, our results suggest that lateral exchange processes, especially on small horizontal scales, are highly relevant for the spatial variability in the soil temperatures and for the simulated extent of surface water bodies, while the effects on the grid-cell mean state and the turbulent exchange with the atmosphere appear to be largely negligible.

The permafrost region stores ∼1300 PgC, and its response to warming is a significant uncertainty in future climate projections. We assess how the treatment of vegetation dynamics and CO₂ fertilisation influences the permafrost carbon feedback (PCF) by performing experiments with the land-surface model ICON-Land. A vertically explicit implementation of the YASSO soil-carbon scheme resolves depth-dependent carbon pools, as well as temperature and moisture controls on decomposition, while a newly introduced cold-adapted shrub plant-functional type (PFT) enables a more realistic description of Arctic vegetation. Using CMIP5-derived climate forcing, we performed nine offline experiments from the year 2020 to 2299 CE under RCP 2.6, 4.5 and 8.5. The experiments isolate the effects of (a) climate warming with fixed 2019 CO₂ and vegetation, (b) rising CO₂ with constant vegetation cover, and (c) the effects of both rising CO₂ and a fully dynamic vegetation. All simulations start from a data-constrained pre-industrial permafrost soil carbon inventory. When vegetation is held static, strong warming (RCP 8.5) alone drives a loss of ∼650 PgC by 2300 CE, turning the permafrost region from a modest sink of carbon into a strong source. Allowing CO₂ fertilisation but no vegetation change reduces the loss to ∼250 PgC because enhanced NPP increases litter inputs. In the fully dynamic experiments, shrubification and northward tree expansion dramatically increase aboveground biomass (of up to 199 PgC) and litterfall, limiting soil-C loss to only ~70 PgC. Consequently, total permafrost-region carbon declines by < 100 PgC under RCP 8.5 and even shows a net gain under the low-emission pathways (RCPs 2.6 and 4.5). These results demonstrate that the sign and magnitude of the PCF are highly sensitive to the representation of vegetation dynamics. Dynamic competition among PFTs, CO₂-driven NPP enhancement, and the resulting shifts in carbon accumulation together can offset most of the carbon released by thawing soils. Incorporating realistic Arctic vegetation, especially cold-tolerant shrubs, is therefore decisive for reliable projections of the permafrost carbon feedback and its impact on the global carbon cycle.

The permafrost region stores ∼1300 PgC, and its response to warming is a significant uncertainty in future climate projections. We assess how the treatment of vegetation dynamics and CO₂ fertilisation influences the permafrost carbon feedback (PCF) by performing experiments with the land-surface model ICON-Land. A vertically explicit implementation of the YASSO soil-carbon scheme resolves depth-dependent carbon pools, as well as temperature and moisture controls on decomposition, while a newly introduced cold-adapted shrub plant-functional type (PFT) enables a more realistic description of Arctic vegetation. Using CMIP5-derived climate forcing, we performed nine offline experiments from the year 2020 to 2299 CE under RCP 2.6, 4.5 and 8.5. The experiments isolate the effects of (a) climate warming with fixed 2019 CO₂ and vegetation, (b) rising CO₂ with constant vegetation cover, and (c) the effects of both rising CO₂ and a fully dynamic vegetation. All simulations start from a data-constrained pre-industrial permafrost soil carbon inventory. When vegetation is held static, strong warming (RCP 8.5) alone drives a loss of ∼650 PgC by 2300 CE, turning the permafrost region from a modest sink of carbon into a strong source. Allowing CO₂ fertilisation but no vegetation change reduces the loss to ∼250 PgC because enhanced NPP increases litter inputs. In the fully dynamic experiments, shrubification and northward tree expansion dramatically increase aboveground biomass (of up to 199 PgC) and litterfall, limiting soil-C loss to only ~70 PgC. Consequently, total permafrost-region carbon declines by < 100 PgC under RCP 8.5 and even shows a net gain under the low-emission pathways (RCPs 2.6 and 4.5). These results demonstrate that the sign and magnitude of the PCF are highly sensitive to the representation of vegetation dynamics. Dynamic competition among PFTs, CO₂-driven NPP enhancement, and the resulting shifts in carbon accumulation together can offset most of the carbon released by thawing soils. Incorporating realistic Arctic vegetation, especially cold-tolerant shrubs, is therefore decisive for reliable projections of the permafrost carbon feedback and its impact on the global carbon cycle.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

The permafrost region stores ∼1300 PgC, and its response to warming is a significant uncertainty in future climate projections. We assess how the treatment of vegetation dynamics and CO₂ fertilisation influences the permafrost carbon feedback (PCF) by performing experiments with the land-surface model ICON-Land. A vertically explicit implementation of the YASSO soil-carbon scheme resolves depth-dependent carbon pools, as well as temperature and moisture controls on decomposition, while a newly introduced cold-adapted shrub plant-functional type (PFT) enables a more realistic description of Arctic vegetation. Using CMIP5-derived climate forcing, we performed nine offline experiments from the year 2020 to 2299 CE under RCP 2.6, 4.5 and 8.5. The experiments isolate the effects of (a) climate warming with fixed 2019 CO₂ and vegetation, (b) rising CO₂ with constant vegetation cover, and (c) the effects of both rising CO₂ and a fully dynamic vegetation. All simulations start from a data-constrained pre-industrial permafrost soil carbon inventory. When vegetation is held static, strong warming (RCP 8.5) alone drives a loss of ∼650 PgC by 2300 CE, turning the permafrost region from a modest sink of carbon into a strong source. Allowing CO₂ fertilisation but no vegetation change reduces the loss to ∼250 PgC because enhanced NPP increases litter inputs. In the fully dynamic experiments, shrubification and northward tree expansion dramatically increase aboveground biomass (of up to 199 PgC) and litterfall, limiting soil-C loss to only ~70 PgC. Consequently, total permafrost-region carbon declines by < 100 PgC under RCP 8.5 and even shows a net gain under the low-emission pathways (RCPs 2.6 and 4.5). These results demonstrate that the sign and magnitude of the PCF are highly sensitive to the representation of vegetation dynamics. Dynamic competition among PFTs, CO₂-driven NPP enhancement, and the resulting shifts in carbon accumulation together can offset most of the carbon released by thawing soils. Incorporating realistic Arctic vegetation, especially cold-tolerant shrubs, is therefore decisive for reliable projections of the permafrost carbon feedback and its impact on the global carbon cycle.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.