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Arctic snow cover extent (SCE) trends and rates of change reported across recent climate assessments vary due to the time period of available data, the selection of snow products, and methodological considerations. While all reported trends are strongly negative during spring, more uncertainty exists in autumn. Motivated to increase the confidence in SCE trends reported in climate assessments, we quantify the impact of (1) year-over-year increases in time series length over the past 2 decades, (2) the choice of reference period, (3) the application of a statistical methodology to improve inter-dataset agreement, (4) the dataset ensemble size, and (5) product version changes. Results show that the rate of change during May and June has remained consistent over the past decade as time series length has increased and is largely insensitive to the choice of reference period. Although new product versions have increased spatial resolution, use more advanced reanalysis meteorology to force snow models, and include improved remote sensing retrieval algorithms, these enhancements do not result in any notable changes in the observed rate of Arctic SCE change in any month compared to a baseline set of older products. The most impactful analysis decision involves the scaling of dataset climatologies using an updated version of the NOAA snow chart climate data record as the baseline. While minor for most months, this adjustment can influence the calculated rate of change for June by a factor of 2 relative to different climatological baselines.

Climate change-driven reductions in sea ice have facilitated increased shipping traffic volumes across the Arctic. Here, we use climate model simulations to investigate changing navigability in the Canadian Arctic for major trade routes and coastal community resupply under 1, 2 and 4 °C of global warming above pre-industrial levels, on the basis of operational Polar Code regulations. Profound shifts in ship-accessible season length are projected across the Canadian Arctic, with the largest increases in the Beaufort region (100–200 d at 2 °C to 200–300 d at 4 °C). Projections along the Northwest Passage and Arctic Bridge trade routes indicate 100% navigation probability for part of the year, regardless of vessel type, above 2 °C of global warming. Along some major trade routes, substantial increases to season length are possible if operators assume additional risk and operate under marginally unsafe conditions. Local changes in accessibility for maritime resupply depend strongly on community location. Shipping routes through the Canadian Arctic are examined under 1, 2 and 4 °C global warming across four vessel classes, including ice breakers, Arctic community resupply ships, and passenger and private vessels. All routes show longer shipping seasons and navigability as a result of sea ice loss.

The western cordillera supplies freshwater across much of western Canada mainly through meltwater from snow and ice. This “alpine water tower” has been, and is projected to be, associated with changes in the seasonality and amount of freshwater availability, which are critical in supporting the societal and environmental flow needs of the region. This study incorporates existing information to synthesize and evaluate current and future freshwater supplies and demands across major north-, west-, and east-flowing sub-basins of the Canadian western cordillera. The assessment of supply indicators reveals several historical changes that are projected to continue, and be exacerbated, particularly by the end of this century and under a high emission scenario. The greatest and most widespread impact is the seasonality of streamflow characterized by earlier spring freshets, increased winter, and decreased summer flow. Future winter and spring warming over all basins will result in decreases in end of season snow and glacier mass balance with greatest declines in more southern regions. In many areas, there will be a greater likelihood of summer freshwater shortages. All sub-basins have environmental and economic freshwater demands and pressures, especially in more southern watersheds where population and infrastructure are more prevalent and industrial, agricultural, and water energy needs are higher. Concerns regarding the continued ability to maintain suitable aquatic habitats and adequate water quality are issues across all regions. These water supply changes along with continued and increasing demands will combine to create a variety of freshwater vulnerabilities across all regions of western Canada. Southern basins including the South Saskatchewan and Okanagan are likely to experience the greatest vulnerabilities due to future summer freshwater supply shortages and increasing economic demands. In more northern areas, vulnerabilities primarily relate to how the rapidly changing landscape (mainly associated with permafrost thaw) impacts freshwater quantity and quality. These vulnerabilities will require various adaptation measures in response to alterations in the timing and amount of future freshwater supplies and demands.

Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.Previous GlobSnow SWE products used a constant snow density of 0.24 kg m-3 to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m-3 early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.

Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade-1 and −1.8 cm (±0.8) cm decade−1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009&ndashl2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

In situ observations of snow water equivalent (SWE) from manual snow surveys and automated sensors are made at approximately 1000 sites across Canada in support of water resource planning for flood...

The drought in the Sahel in the 1980s has been associated with Indian Ocean warming, although the Sahel has experienced a recovery in precipitation since the 1990s, despite continued warming in the Indian Ocean. Using the Community Earth System Model (CESM), we examined the linkages between the pattern of Indian Ocean warming and changes in atmospheric circulation over the Indian Ocean and North Africa to determine how they impact Sahel precipitation. The influence of the Indian Ocean on Sahel precipitation was investigated using a series of sea surface temperature (SST) sensitivity experiments. We identified two mechanisms by which the Indian Ocean can alter Sahel precipitation. The first mechanism is associated with perturbations in SSTs on the equator that alter Sahel precipitation by modulating the Asian monsoon circulation and driving changes in descent in North Africa. The second mechanism is associated with SST perturbations that cover more of the basin and alter the overturning circulation between the Indian and Atlantic Oceans. These two mechanisms result in different precipitation responses in the Sahel: the first induces an increase in precipitation as a result of warming in the Indian Ocean, whereas the second produces a decrease in Sahel precipitation in response to warming. Our results suggest that obtaining robust projections of precipitation in the Sahel will require reliably capturing the scale and spatial patterns of Indian Ocean warming.

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

Conventional wisdom holds that confidence in future projections of snow cover extent and snow mass requires an understanding of the expected changes in future snow characteristics as a function of modelled snow processes. We will highlight contrasting results which suggest differing importance in the role of sub-grid scale processes on simulations of seasonal snow.The first study is an evaluation of simulated snow cover extent projections from models participating in the 6th phase of the World Climate Research Programme Coupled Model Inter-comparison Project (CMIP-6). We demonstrate a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all future climate scenarios. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8% relative to the 1995-2014 level per °C of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast response components of the cryosphere such as sea ice and near surface permafrost.The second study makes use of an ensemble of land surface models, downscaled to 5 km resolution across North America over the 2009-2017 period. In this case, uncertainty in total North American snow mass is dominated by differences among land surface model configurations. While the largest absolute spread in snow mass is found in mountainous regions, heavily vegetated boreal regions have the largest fractional spread compared to climatological values. In particular, differences in rain-snow partitioning and sublimation rates control the largest portions of the total uncertainty. These results suggest that projections of future snow mass depend specifically on how such processes are modelled and parameterized.

This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover1–3. These changes in snow cover affect Earth’s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere4–6. In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking7–9. Here we use the new GlobSnow 3.0 dataset to show that the 1980–2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 ± 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40° N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433–3,380 gigatonnes (mean 2,867) to 2,846–3,062 gigatonnes (mean 2,938)—a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia; both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth’s energy, water and carbon budgets. Applying a bias correction to a state-of-the-art dataset covering non-alpine regions of the Northern Hemisphere and to three other datasets yields a more constrained quantification of snow mass in March from 1980 to 2018.

Seven gridded northern hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Inter-comparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS; the European Centre for Medium-Range Forecasts interim land surface reanalysis – ERA-land; the NASA Modern-Era Retrospective Analysis for Research and Applications – MERRA; the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) standalone passive microwave retrievals (NASA AMSR-E historical and operational algorithms) which do not utilize surface snow observations. Evaluation included comparisons against independent surface observations from Russia, Finland, and Canada, and calculation of spatial and temporal correlations in SWE anomalies. The standalone passive microwave SWE products (AMSR-E historical and operational SWE algorithms) exhibit low spatial and temporal correlations to other products, and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides comparable performance to the reanalysis-based products; RMSEs over Finland and Russia for all but the AMSR-E products is ~50 mm or less. Using a four-dataset ensemble that excluded the standalone passive microwave products reduced the RMSE by 10 mm (20%) and increased the correlation by 0.1; ensembles that contain Crocus and/or MERRA perform better than those that do not. The observed RMSE of the best performing datasets is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

The relationship between land surface temperature and snow cover extent trends is examined in three distinct types of ensembles over the 1981-2010 period: an observation-based ensemble, a representative selection of CMIP5 coupled climate model output, and two large initial condition coupled climate model ensembles. Observation-based estimates of snow cover sensitivity are stronger than simulated over midlatitude and alpine regions. Observed sensitivity estimates over Arctic regions are consistent with simulated values. Anomalous snow cover extend trends present in one dataset, the NOAA climate record, obscure the relationship to surface temperature seen in the rest of the analyzed data. The spread in modeled snow cover trends reflects roughly equal contributions from inter-model variability and from natural variability. Together, the anomalous relationship between surface temperature and snow cover expressed in the NOAA climate record and the large influence of natural variability present in the simulations highlight the importance of ensemble-based approaches.

Better constraining the continental climate response to anthropogenic forcing is essential to improve climate projections. In this study, pattern scaling is used to extract, from observations, the patterned response of sea surface temperature (SST) and sea ice concentration (SICE) to anthropogenically dominated long-term global warming. The SST response pattern includes a warming of the tropical Indian Ocean, the high northern latitudes, and the western boundary currents. The SICE pattern shows seasonal variations of the main locations of sea ice loss. These SST–SICE response patterns are used to drive an ensemble of an atmospheric general circulation model, the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5), over the period 1980–2010 along with a standard AMIP ensemble using observed SST—SICE. The simulations enable attribution of a variety of observed trends of continental climate to global warming. On the one hand, the warming trends observed in all seasons across the entire Northern Hemisphere extratropics result from global warming, as does the snow loss observed over the northern midlatitudes and northwestern Eurasia. On the other hand, 1980–2010 precipitation trends observed in winter over North America and in summer over Africa result from the recent decreasing phase of the Pacific decadal oscillation and the recent increasing phase of the Atlantic multidecadal oscillation, respectively, which are not part of the global warming signal. The method holds promise for near-term decadal climate prediction but as currently framed cannot distinguish regional signals associated with oceanic internal variability from aerosol forcing and other sources of short-term forcing.

Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes against local and global observations in a wide variety of settings, including snow schemes that are included in Earth System Models. The project aims at identifying crucial processes and snow characteristics that need to be improved in snow models in the context of local- and global-scale modeling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. ESM-SnowMIP is tightly linked to the Land Surface, Snow and Soil Moisture Model Intercomparison Project, which in turn is part of the 6th phase of the Coupled Model Intercomparison Project (CMIP6).

Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

Heat transport between the surface and deep ocean strongly influences transient climate change. Mechanisms setting this transport are investigated using coupled climate models and by projecting ocean circulation into the temperature-depth diagram. In this diagram, a “cold cell” cools the deep ocean through the downwelling of Antarctic waters and upwelling of warmer waters and is balanced by warming due to a “warm cell,” coincident with the interhemispheric overturning and previously linked to wind and haline forcing. With anthropogenic warming, the cold cell collapses while the warm cell continues to warm the deep ocean. Simulations with increasingly strong warm cells, set by their mean Southern Hemisphere winds, exhibit increasing deep-ocean warming in response to the same anthropogenic forcing. It is argued that the partition between components of the circulation which cool and warm the deep ocean in the preindustrial climate is a key determinant of ocean vertical heat transport with global warming.

This study seeks to derive the sea surface temperature (SST) response to anthropogenic forcing from observations over the last century, using simple methods inspired from pattern scaling. As in pattern scaling, the spatial response is assumed to scale with global-mean and annual-mean surface temperature. The long-term aim of this work is to generate anthropogenically forced SST and sea ice patterns for the recent past and near-term future, and use them to force atmosphere–land climate models for attribution and prediction purposes. The present work compares estimation methodologies and, within a Monte Carlo framework based on large initial condition ensembles of climate model simulations, examines the robustness of the patterns obtained. The different methods explored here yield a similar SST spatial response, mostly reflecting the observed SST linear trend map. The different methods nevertheless provide distinctive temporal evolution of the global-mean and annual-mean SST response, which in turn affects the temporal evolution of the global-mean and annual-mean air surface temperature simulated in corresponding prescribed SST simulations. The estimated SST spatial response consists mostly of a warming of the midlatitude coasts near the western boundary currents, the tropical Indian Ocean, and the Arctic Ocean. This pattern generally agrees with previously published observational and modeling studies. Based on Monte Carlo analysis of the large ensembles, it is found that between 36% and 56% of its spatial variance results from anthropogenic forcing. Overall, the work herein provides constraints on the uncertainty associated with the spatial variability of an anthropogenically forced component of climate change derived from observations, which can potentially be used for climate attribution and prediction.

Because the rain falls and the wind blows Global warming is expected to intensify the hydrological cycle, but it might also make the atmosphere less energetic. Laliberté et al. modeled the atmosphere as a classical heat engine in order to evaluate how much energy it contains and how much work it can do (see the Perspective by Pauluis). They then used a global climate model to project how that might change as climate warms. Although the hydrological cycle may increase in intensity, it does so at the expense of its ability to do work, such as powering large-scale atmospheric circulation or fueling more very intense storms. Science , this issue p. 540 ; see also p. 475

The Canadian Sea Ice and Snow Evolution Network (CanSISE) is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multiyear ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last eight years. The multimodel consensus over the 2020–2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10 % per decade (or 15–30 % in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10 % per decade (30 % in total) are projected across southern Canada.

This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth-System Models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive, and initial condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea-ice trends there. The strengths and weaknesses of the modeling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea-ice thickness initialization using statistical predictors available in real time.

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.

We describe a newly developed hydrodynamic code for studying accretion disk processes. The numerical method uses a finite volume, nonlinear, Total Variation Diminishing (TVD) scheme to capture shocks and control spurious oscillations. It is second-order accurate in time and space and makes use of a FARGO-type algorithm to alleviate Courant-Friedrichs-Lewy time step restrictions imposed by the rapidly rotating inner disk region. OpenMP directives are implemented enabling faster computations on shared-memory, multi-processor machines. The resulting code is simple, fast and memory efficient. We discuss the relevant details of the numerical method and provide results of the code's performance on standard test problems. We also include a detailed examination of the code's performance on planetary disk-planet interactions. We show that the results produced on the standard problem setup are consistent with a wide variety of other codes., 19 pages, 17 figures

The global water cycle leaves an imprint on ocean salinity through evaporation and precipitation. It has been proposed that observed changes in salinity can be used to infer changes in the water cycle. Here salinity is characterized by the distribution of water masses in salinity coordinates. Only mixing and sources and sinks of freshwater and salt can modify this distribution. Mixing acts to collapse the distribution, making saline waters fresher and fresh waters more saline. Hence, in steady state, there must be net precipitation over fresh waters and net evaporation over saline waters. A simple model is developed to describe the relationship between the breadth of the distribution, the water cycle, and mixing—the latter being characterized by an e-folding time scale. In both observations and a state-of-the-art ocean model, the water cycle maintains a salinity distribution in steady state with a mixing time scale of the order of 50 yr. The same simple model predicts the response of the salinity distribution to a change in the water cycle. This study suggests that observations of changes in ocean salinity could be used to infer changes in the hydrological cycle.

[1] Time scales derived from annular mode (AM) variability provide dynamical insight into stratosphere-troposphere coupling and are linked to the strength of AM responses to climate forcings. AM time scales reflect decorrelation times of geopotential height in the stratosphere and troposphere. But geopotential height involves a vertical integral via the hypsometric equation, and this makes ambiguous some aspects of the dependence of the time scales on vertical level. In this study, a method for decomposing AM variability into contributions from surface pressure and from temperature is presented that is based on a linearization of the hypsometric equation. The decomposition is then used to interpret stratosphere-troposphere coupling events and the seasonal variation of AM time scales in reanalysis products and in two versions of a general circulation model that have distinctly different stratospheric representation. Surface pressure variations best account for tropospheric AM variability and stratospheric temperature variations best account for stratospheric AM variability during coupling events. But AM time scales are not so readily separated because they involve strong coupling between the surface pressure and stratospheric temperature variations: the pressure-temperature cross-correlation functions are small in magnitude but highly persistent and thus provide significant sources of AM persistence. These empirical results provide a basis for further theoretical analysis on the origins of zonal mean stratosphere-troposphere coupling.

A planet orbiting around a star in a binary system can be ejected if it lies too far from its host star. We find that instability boundaries first obtained in numerical studies can be explained by overlap between sub-resonances within mean-motion resonances (mostly of the j:1 type). Strong secular forcing from the companion displaces the centroids of different sub-resonances, producing large regions of resonance overlap. Planets lying within these overlapping regions experience chaotic diffusion, which in most cases leads to their eventual ejection. The overlap region extends to shorter-period orbits as either the companion's mass or its eccentricity increase. Our analytical calculations reproduce the instability boundaries observed in numerical studies and yield the following two additional results. Firstly, the instability boundary as a function of eccentricity is jagged; thus, the widest stable orbit could be reduced from previously quoted values by as much as 20%. Secondly, very high order resonances (e.g., 50:3) do not significantly modify the instability boundary despite the fact that these weak resonances can produce slow chaotic diffusion which may be missed by finite-duration numerical integrations. We present some numerical evidence for the first result. More extensive experiments are called for to confirm these conclusions. For the special case of circular binaries, we find that the Hill criterion (based on the critical Jacobi integral) yields an instability boundary that is very similar to that obtained by resonance overlap arguments, making the former both a necessary and a sufficient condition for planet instability., Accepted for publication in the Astrophyical Journal. Consists of 8 pages, 6 figures

We present test simulations of a TVD hydrodynamical code designed with very few calculations per time step. The code is to be used to preform simulations of proto‐planet interactions within gas disks in early solar systems.