A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil)

Peatlands have often been neglected in Earth System Models (ESMs). Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material), and purely organic soil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrum of soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation - and thus peatland formation, degradation and stability – is integrated in the vertically-resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES, and compare this to measured peat age-depth profiles. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss along with soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage. These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils, and realistic profiles of soil organic carbon for peatlands. In particular the best performing configurations had root mean squared error (RMSE) in carbon density for peat sites of 7.7–16.7 kgC m−3 depending on climate zone, when compared against typical peat profiles based on 216 sites from a global dataset of peat cores. This error is considerably smaller than the soil carbon itself (around 30–60 kgC m−3) and reduced by 35–80 % compared with standard JULES. The RMSE at mineral soil sites is also smaller in JULES-Peat than JULES itself (reduced by ~30–50 %). Thus JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not require any additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils, and associated carbon-cycle feedbacks in ESMs, which other land surface models could follow.

Advances in modelling large river basins in cold regions with Modélisation Environmentale Communautaire - Surface and Hydrology (MESH), the Canadian hydrological land surface scheme

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 2: Global benchmarking

The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open-source community model designed to address research questions that explore the role of the land surface in the global climate system. Here, we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing, we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Future model development should address (i) a positive albedo bias and resulting shortwave radiation bias in parts of the Northern Hemisphere (NH) extratropics and Tibetan Plateau, (ii) an out-of-phase seasonal gross primary productivity cycle in the humid tropics of South America and Africa, (iii) a lacking spatial correlation of annual mean net ecosystem exchange with site-level measurements, (iv) an underestimation of fractional area burned and corresponding emissions in the boreal forests, (v) a negative soil organic carbon bias in high latitudes, and (vi) a time lag in seasonal leaf area index maxima in parts of the NH extratropics. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.

Simulating shrubs and their energy and carbon dioxide fluxes in Canada's Low Arctic with the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC)

The Arctic is warming more rapidly than other regions of the world leading to ecosystem change including shifts in vegetation communities, permafrost degradation and alteration of tundra surface-atmosphere energy and carbon (C) fluxes, among others. However, year-round C and energy flux measurements at high-latitude sites remain rare. This poses a challenge for evaluating the impacts of climate change on Arctic tundra ecosystems and for developing and evaluating process-based models, which may be used to predict regional and global energy and C feedbacks to the climate system. Our study used 14 years of seasonal eddy covariance (EC) measurements of carbon dioxide (CO2), water and energy fluxes and winter soil chamber CO2 flux measurements at a dwarf-shrub tundra site underlain by continuous permafrost in Canada's Southern Arctic ecozone to evaluate the incorporation of shrub plant functional types (PFTs) in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC), the land surface component of the Canadian Earth System Model. In addition to new PFTs, a modification of the efficiency with which water evaporates from the ground surface was applied. This modification addressed a high ground evaporation bias that reduced model performance when soils became very dry, limited heat flow into the ground and reduced plant productivity through water stress effects. Compared to the grass and tree PFTs previously used by CLASSIC to represent the vegetation in Arctic permafrost-affected regions, simulations with the new shrub PFTs better capture the physical and biogeochemical impact of shrubs on the magnitude and seasonality of energy and CO2 fluxes at the dwarf-shrub tundra evaluation site. The revised model, however, tends to overestimate gross primary productivity, particularly in spring, and overestimated late winter CO2 emissions. On average, annual net ecosystem CO2 exchange was positive for all simulations, suggesting this site was a net CO2 source of 18 ± 4 g C m−2 year−1 using shrub PFTs, 15 ± 6 g C m−2 year−1 using grass PFTs, and 25 ± 5 g C m−2 year−1 using tree PFTs. These results highlight the importance of using appropriate PFTs in process-based models to simulate current and future Arctic surface-atmosphere interactions.

Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.