Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme

Abstract. The Arctic is warming rapidly, especially in winter, which is causing large-scale reductions in snow cover. Snow is one of the main controls on soil thermodynamics, and changes in its thickness and extent affect both permafrost thaw and soil biogeochemistry. Since soil respiration during the cold season potentially offsets carbon uptake during the growing season, it is essential to achieve a realistic simulation of the effect of snow cover on soil conditions to more accurately project the direction of arctic carbon-climate feedbacks under continued winter warming. The Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model has used – up until now – a single layer snow scheme, which underestimated the insulation effect of snow, leading to a cold bias in soil temperature. To address this shortcoming, we developed and integrated a dynamic, multi-layer snow scheme in LPJ-GUESS. The new snow scheme performs well in simulating the insulation of snow at hundreds of locations across Russia compared to observations. We show that improving this single physical factor enhanced simulations of permafrost extent compared to an advanced permafrost product. Besides soil thermodynamics, the new snow scheme resulted in increased winter respiration and an overall lower soil carbon content due to warmer soil conditions. The Dynamic scheme also influenced vegetation dynamics, resulting in an improved vegetation distribution and tundra-taiga boundary simulation. This study highlights the importance of a correct representation of snow in ecosystem models to project biogeochemical processes that govern climate feedbacks. The new dynamic snow scheme is an essential improvement in the simulation of cold season processes, which reduces the uncertainty of model projections. These developments contribute to a better understanding of the Arctic's role in the global climate system.

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Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS

Biogeosciences ◽

10.5194/bg-18-5767-2021 ◽

2021 ◽

Vol 18 (20) ◽

pp. 5767-5787

Author(s):

Alexandra Pongracz ◽

David Wårlind ◽

Paul A. Miller ◽

Frans-Jan W. Parmentier

Keyword(s):

Snow Cover ◽

Large Scale ◽

Single Layer ◽

Physical Factor ◽

Cold Season ◽

The Arctic ◽

Soil Conditions ◽

Cold Bias ◽

Climate Feedbacks ◽

Realistic Simulation

Abstract. The Arctic is warming rapidly, especially in winter, which is causing large-scale reductions in snow cover. Snow is one of the main controls on soil thermodynamics, and changes in its thickness and extent affect both permafrost thaw and soil biogeochemistry. Since soil respiration during the cold season potentially offsets carbon uptake during the growing season, it is essential to achieve a realistic simulation of the effect of snow cover on soil conditions to more accurately project the direction of arctic carbon–climate feedbacks under continued winter warming. The Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model has used – up until now – a single layer snow scheme, which underestimated the insulation effect of snow, leading to a cold bias in soil temperature. To address this shortcoming, we developed and integrated a dynamic, multi-layer snow scheme in LPJ-GUESS. The new snow scheme performs well in simulating the insulation of snow at hundreds of locations across Russia compared to observations. We show that improving this single physical factor enhanced simulations of permafrost extent compared to an advanced permafrost product, where the overestimation of permafrost cover decreased from 10 % to 5 % using the new snow scheme. Besides soil thermodynamics, the new snow scheme resulted in a doubled winter respiration and an overall higher vegetation carbon content. This study highlights the importance of a correct representation of snow in ecosystem models to project biogeochemical processes that govern climate feedbacks. The new dynamic snow scheme is an essential improvement in the simulation of cold season processes, which reduces the uncertainty of model projections. These developments contribute to a more realistic simulation of arctic carbon–climate feedbacks.

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Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

Earth System Dynamics ◽

10.5194/esd-8-963-2017 ◽

2017 ◽

Vol 8 (4) ◽

pp. 963-976 ◽

Cited By ~ 15

Author(s):

Jaak Jaagus ◽

Mait Sepp ◽

Toomas Tamm ◽

Arvo Järvet ◽

Kiira Mõisja

Keyword(s):

Time Series ◽

Snow Cover ◽

Atmospheric Circulation ◽

Air Temperature ◽

Large Scale ◽

Regime Shifts ◽

The Arctic ◽

Dry Period ◽

Snow Cover Duration ◽

Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

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Variability of the Cold Season Climate in Central Asia. Part II: Hydroclimatic Predictability

Journal of Climate ◽

10.1175/jcli-d-18-0892.1 ◽

2019 ◽

Vol 32 (18) ◽

pp. 6015-6033 ◽

Cited By ~ 5

Author(s):

Lars Gerlitz ◽

Eva Steirou ◽

Christoph Schneider ◽

Vincent Moron ◽

Sergiy Vorogushyn ◽

...

Keyword(s):

Central Asia ◽

Snow Cover ◽

Large Scale ◽

Cold Season ◽

Hadley Cell ◽

Quasi Biennial Oscillation ◽

Large Variability ◽

Forecast Performance ◽

Eurasian Snow Cover ◽

Eurasian Snow

Abstract Central Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November–March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Niño. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions.

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The Frontier Research System for Global Change—The International Arctic Research Center (Frontier-IARC): its Origin and Tentative Science Plan

Marine Technology Society Journal ◽

10.4031/mtsj.33.1.10 ◽

1999 ◽

Vol 33 (1) ◽

pp. 81-84

Author(s):

Jinro Ukila ◽

Moloyoshi Ikeda

Keyword(s):

Climate Change ◽

Global Change ◽

Global Climate Change ◽

Large Scale ◽

Global Climate ◽

Research Effort ◽

Arctic Region ◽

Research Center ◽

The Arctic ◽

Research System

The Frontier Research System for Global Change—the International Arctic Research Center (Frontier-IARC) is a research program funded by the Frontier Research System for Global Change. The program is jointly run under a cooperative agreement between the Frontier Research System for Global Change and the University of Alaska Fairbanks. The aim of the program is to understand the role of the Arctic region in global climate change. The program concentrates its research effort initially on the areas of air-sea-ice interactions, bio-geochemical processes and the ecosystem. To understand the arctic climate system in the context of global climate change, we focus on mechanisms controlling arctic-subarctic interactions, and identify three key components: the freshwater balance, the energy balance, and the large-scale atmospheric processes. Knowledge of details of these components and their interactions will be gained through long-term monitoring, process studies, and modeling; our focus will be on the latter two categories.

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Pan-Eurasian Experiment (PEEX): towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-14421-2016 ◽

2016 ◽

Vol 16 (22) ◽

pp. 14421-14461 ◽

Cited By ~ 27

Author(s):

Hanna K. Lappalainen ◽

Veli-Matti Kerminen ◽

Tuukka Petäjä ◽

Theo Kurten ◽

Aleksander Baklanov ◽

...

Keyword(s):

Research Agenda ◽

Large Scale ◽

System Analysis ◽

Global Climate ◽

The Arctic ◽

Research Approach ◽

Natural Environments ◽

System Perspective ◽

Holistic Understanding ◽

Eurasian Region

Abstract. The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic–boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context.

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Cold season emissions dominate the Arctic tundra methane budget

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1516017113 ◽

2015 ◽

Vol 113 (1) ◽

pp. 40-45 ◽

Cited By ~ 149

Author(s):

Donatella Zona ◽

Beniamino Gioli ◽

Róisín Commane ◽

Jakob Lindaas ◽

Steven C. Wofsy ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Terrestrial Ecosystems ◽

Arctic Tundra ◽

Cold Season ◽

Global Climate Models ◽

The Arctic ◽

Late Season ◽

Aircraft Data

Arctic terrestrial ecosystems are major global sources of methane (CH4); hence, it is important to understand the seasonal and climatic controls on CH4 emissions from these systems. Here, we report year-round CH4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the “zero curtain” period, when subsurface soil temperatures are poised near 0 °C. The zero curtain may persist longer than the growing season, and CH4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH4 derived from aircraft data demonstrate the large spatial extent of late season CH4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH4 y−1, ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

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Attributing the global increase in permafrost temperatures to human induced climate change

10.5194/egusphere-egu21-8337 ◽

2021 ◽

Author(s):

Lukas Gudmundsson ◽

Josefine Kirchner ◽

Anne Gädeke ◽

Eleanor Burke ◽

Boris K. Biskaborn ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Global Climate ◽

Volcanic Eruptions ◽

Global Climate Models ◽

Anthropogenic Climate Change ◽

Permafrost Degradation ◽

Climate Feedbacks ◽

Permafrost Temperature

Permafrost temperatures are increasing at the global scale, resulting in permafrost degradation. Besides substantial impacts on Arctic and Alpine hydrology and the stability of landscapes and infrastructure, permafrost degradation can trigger a large-scale release of carbon to the atmosphere with possible global climate feedbacks. Although increasing global air temperature is unanimously linked to human emissions into the atmosphere, the attribution of observed permafrost warming to anthropogenic climate change has so far mostly relied on anecdotal evidence. Here we apply a climate change detection and attribution approach to long permafrost temperature records from 15 boreholes located in the northern Hemisphere and simulated soil temperatures obtained from global climate models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We show that observed and simulated trends in permafrost temperature are only consistent if the effect of human emissions on the climate system is considered in the simulations. Moreover, the analysis also reveals that neither simulated pre-industrial climate variability nor the effects natural drivers of climate change (e.g. impacts of large volcanic eruptions) suffice to explain the observed trends. While these results are most significant for a global mean assessment, our analysis also reveals that simulated effects of anthropogenic climate change on permafrost temperature are also consistent with the observed record at the station scale. In summary, the quantitative combination of observed and simulated evidence supports the conclusion that anthropogenic climate change is the key driver of increasing permafrost temperatures with implications for carbon cycle-climate feedbacks at the planetary scale.

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Surface reflectivity in polar regions retrieved from TDS-1 mission data

10.5194/egusphere-egu21-13036 ◽

2021 ◽

Author(s):

Frederik Kreß ◽

Maximilian Semmling ◽

Estel Cardellach ◽

Weiqiang Li ◽

Mainul Hoque ◽

...

Keyword(s):

Remote Sensing ◽

Surface Roughness ◽

Sea Ice ◽

Large Scale ◽

Global Climate ◽

Path Loss ◽

Signal Strength ◽

Global Navigation Satellite Systems ◽

The Arctic ◽

Polar Regions

In current times of a changing global climate, a special interest is focused on the large-scale recording of sea ice. Among the existing remote sensing methods, bi- statically reflected signals of Global Navigation Satellite Systems (GNSS) could play an important role in fulfilling the task. Within this project, sensitivity of GNSS signal reflections to sea ice properties like its occurrence, sea ice thick- ness (SIT) and sea concentration (SIC) is evaluated. When getting older, sea ice tends go get thicker. Because of decreasing salinity, i.e. less permittivity, as well as relatively higher surface roughness of older ice, it can be assumed that reflected signal strength decreases with increasing SIT. The reflection data used were recorded in the years 2015 and 2016 by the TechDemoSat-1 (TDS-1) satellite over the Arctic and Antarctic. It includes a down-looking antenna for the reflected as well as an up-looking antenna dedicated to receive the direct sig- nal. The raw data, provided by the manufacturer SSTL, were pre-processed by IEEC/ICE-CSIC to derive georeferenced signal power values. The reflectivity was estimated by comparing the power of the up- and down-looking links. The project focuses on the signal link budget to apply necessary corrections. For this reason, the receiver antenna gain as well as the Free-Space Path Loss (FSPL) were calculated and applied for reflectivity correction. Differences of nadir and zenith antenna FSPL and gain show influence of up to 6 dB and &#8722;9 dB to 9 dB respectively on the recorded signal strength. All retrieved reflectivity values are compared to model predictions based on Fresnel coefficients but also to avail- able ancillary truth data of other remote sensing missions to identify possible patterns: SIT relations are investigated using Level-2 data of the Soil Moisture and Ocean Salinity (SMOS) satellite. The SIC comparison was done with an AMSR-2 product. The results show sensitivity of the reflectivity value to both SIT and SIC simultaneously, whereby the surface roughness is also likely to have an influence. This on-going study aims at the consolidation of retrieval algorithms for sea-ice observation. The resolution of different ice types and the retrieval of SIT and SIC based on satellite data is a challenge for future work in this respect.

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ESD Reviews: mechanisms, evidence, and impacts of climate tipping elements

10.5194/esd-2020-16 ◽

2020 ◽

Cited By ~ 1

Author(s):

Seaver Wang ◽

Zeke Hausfather

Keyword(s):

Climate Change ◽

Radiative Forcing ◽

Large Scale ◽

Global Climate ◽

Climate Feedbacks ◽

Positive Feedbacks ◽

Climate Risks ◽

Earth Systems ◽

Current Century ◽

Emissions Scenarios

Abstract. Increasing attention is focusing upon climate tipping elements – large-scale earth systems anticipated to respond through positive feedbacks to anthropogenic climate change by shifting towards new long-term states. In some but not all cases, such changes could produce additional greenhouse gas emissions or radiative forcing that could compound global warming. Developing greater understanding of tipping elements is important for predicting future climate risks. Here we review mechanisms, predictions, impacts, and knowledge gaps associated with ten notable climate tipping elements. We also evaluate which tipping elements are more imminent and whether shifts will likely manifest rapidly or over longer timescales. Some tipping elements are significant to future global climate and will likely affect major ecosystems, climate patterns, and/or carbon cycling within the current century. However, assessments under different emissions scenarios indicate a strong potential to reduce or avoid impacts associated with many tipping elements through climate change mitigation. Most tipping elements do not possess the potential for abrupt future change within years, and some tipping elements are perhaps more accurately termed climate feedbacks. Nevertheless, significant uncertainties remain associated with many tipping elements, highlighting an acute need for further research and modeling to better constrain risks.

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On the Extreme Variability and Change of Cold-Season Temperatures in Northwest Canada

Journal of Climate ◽

10.1175/2007jcli1583.1 ◽

2008 ◽

Vol 21 (1) ◽

pp. 94-113 ◽

Cited By ~ 18

Author(s):

Kit K. Szeto

Keyword(s):

North Pacific ◽

Large Scale ◽

Heat Budget ◽

Thermal Response ◽

Cold Season ◽

Temperature Variability ◽

Cold Bias ◽

Latent Heating ◽

Recent Warming ◽

The Cross

Abstract The Mackenzie River basin (MRB) in northwestern Canada is a climatologically important region that exerts significant influences on the weather and climate of North America. The region exhibits the largest cold-season temperature variability in the world on both the intraseasonal and interannual time scales. In addition, some of the strongest recent warming signals have been observed over the basin. To understand the nature of these profound and intriguing observed thermal characteristics of the region, its atmospheric heat budget is assessed by using the NCEP–NCAR reanalysis dataset. The composite heat budgets and large-scale atmospheric conditions that are representative of anomalous winters in the region are examined in unison to study the processes that are responsible for the development of extreme warm/cold winters in the MRB. It is shown that the large winter temperature variability of the region is largely a result of the strong variability of atmospheric circulations over the North Pacific, the selective enhancement/weakening of latent heating of the cross-barrier flow for various onshore flow configurations, and synoptic-scale feedback processes that accentuate the thermal response of the basin to the changes in upwind conditions. The improved understanding of mechanisms that govern the thermal response of the basin to changes in the upstream environment provides a theoretical basis to interpret the climate change and modeling results for the region. In particular, the large recent warming trend observed for the region can be understood as the enhanced response of the basin to the shift in North Pacific circulation regime during the mid-1970s. The strong cold bias that affected the region in some climate model results can be attributed to the underprediction of orographic precipitation and associate latent heating of the cross-barrier flow, and the subsequent weakening of mean subsidence and warming over the basin in the models.

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