NOAA National Ice Center

Abstract The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Why we are losing sea ice Arctic sea ice is disappearing rapidly, leading to predictions of an ice-free summer in the near future. Simulations of the timing of summer sea-ice loss differ substantially, making it difficult to evaluate the pace of the loss. Notz and Stroeve observed a linear relationship between the monthly-mean September sea-ice area and cumulative CO 2 emissions. This allowed them to predict Arctic summer sea ice directly from the observational record. Interestingly, most models underestimate this loss. Science , this issue p. 747

The extent of Arctic perennial sea ice, the year‐round ice cover, was significantly reduced between March 2005 and March 2007 by 1.08 × 10 6 km 2 , a 23% loss from 4.69 × 10 6 km 2 to 3.61 × 10 6 km 2 , as observed by the QuikSCAT/SeaWinds satellite scatterometer (QSCAT). Moreover, the buoy‐based Drift‐Age Model (DM) provided long‐term trends in Arctic sea‐ice age since the 1950s. Perennial‐ice extent loss in March within the DM domain was noticeable after the 1960s, and the loss became more rapid in the 2000s when QSCAT observations were available to verify the model results. QSCAT data also revealed mechanisms contributing to the perennial‐ice extent loss: ice compression toward the western Arctic, ice loading into the Transpolar Drift (TD) together with an acceleration of the TD carrying excessive ice out of Fram Strait, and ice export to Baffin Bay. Dynamic and thermodynamic effects appear to be combining to expedite the loss of perennial sea ice.

Recent warming in the Arctic, which has been amplified during the winter1–3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions. Winter warming in the Arctic will increase the CO2 flux from soils. A pan-Arctic analysis shows a current loss of 1,662 TgC per year over the winter, exceeding estimated carbon uptake in the growing season; projections suggest a 17% increase under RCP 4.5 and a 41% increase under RCP 8.5 by 2100.

Abstract. Greenland ice sheet mass loss has accelerated in the past decade responding to combined glacier discharge and surface melt water runoff increases. During summer, absorbed solar energy, modulated at the surface primarily by albedo, is the dominant factor governing surface melt variability in the ablation area. Using satellite-derived surface albedo with calibrated regional climate modeled surface air temperature and surface downward solar irradiance, we determine the spatial dependence and quantitative impact of the ice sheet albedo feedback over 12 summer periods beginning in 2000. We find that, while albedo feedback defined by the change in net solar shortwave flux and temperature over time is positive over 97% of the ice sheet, when defined using paired annual anomalies, a second-order negative feedback is evident over 63% of the accumulation area. This negative feedback damps the accumulation area response to warming due to a positive correlation between snowfall and surface air temperature anomalies. Positive anomaly-gauged feedback concentrated in the ablation area accounts for more than half of the overall increase in melting when satellite-derived melt duration is used to define the timing when net shortwave flux is sunk into melting. Abnormally strong anticyclonic circulation, associated with a persistent summer North Atlantic Oscillation extreme since 2007, enabled three amplifying mechanisms to maximize the albedo feedback: (1) increased warm (south) air advection along the western ice sheet increased surface sensible heating that in turn enhanced snow grain metamorphic rates, further reducing albedo; (2) increased surface downward shortwave flux, leading to more surface heating and further albedo reduction; and (3) reduced snowfall rates sustained low albedo, maximizing surface solar heating, progressively lowering albedo over multiple years. The summer net infrared and solar radiation for the high elevation accumulation area approached positive values during this period. Thus, it is reasonable to expect 100% melt area over the ice sheet within another similar decade of warming.

The waters of the Colorado River serve 27 million people in seven states and two countries but are overallocated by more than 10% of the river's historical mean. Climate models project runoff losses of 7-20% from the basin in this century due to human-induced climate change. Recent work has shown however that by the late 1800s, decades prior to allocation of the river's runoff in the 1920s, a fivefold increase in dust loading from anthropogenically disturbed soils in the southwest United States was already decreasing snow albedo and shortening the duration of snow cover by several weeks. The degree to which this increase in radiative forcing by dust in snow has affected timing and magnitude of runoff from the Upper Colorado River Basin (UCRB) is unknown. Here we use the Variable Infiltration Capacity model with postdisturbance and predisturbance impacts of dust on albedo to estimate the impact on runoff from the UCRB across 1916-2003. We find that peak runoff at Lees Ferry, Arizona has occurred on average 3 wk earlier under heavier dust loading and that increases in evapotranspiration from earlier exposure of vegetation and soils decreases annual runoff by more than 1.0 billion cubic meters or ∼5% of the annual average. The potential to reduce dust loading through surface stabilization in the deserts and restore more persistent snow cover, slow runoff, and increase water resources in the UCRB may represent an important mitigation opportunity to reduce system management tensions and regional impacts of climate change.

Abstract Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land‐atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO 2 ) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process‐based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century‐long (1901–2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project and two observation‐based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 10 15 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr −1 with a median value of 51 Pg C yr −1 during 2001–2010. The largest uncertainty in SOC stocks exists in the 40–65°N latitude whereas the largest cross‐model divergence in Rh are in the tropics. The modeled SOC change during 1901–2010 ranges from −70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble‐estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO 2 and nitrogen deposition over intact ecosystems increased SOC stocks—even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.

The fate of currently frozen permafrost carbon as high-latitude climate warms remains highly uncertain and existing models give widely varying estimates of the permafrost carbon-climate feedback. This uncertainty is due to many factors, including the role that permafrost thaw-induced transitions in soil hydrologic conditions will have on organic matter decomposition rates and the proportion of aerobic to anaerobic respiration. Large-scale permafrost thaw, as predicted by the Community Land Model (CLM) under an unmitigated greenhouse gas emissions scenario, results in significant soil drying due to increased drainage following permafrost thaw, even though permafrost domain water inputs are projected to rise (net precipitation minus evaporation >0). CLM predicts that drier soil conditions will accelerate organic matter decomposition, with concomitant increases in carbon dioxide (CO _2 ) emissions. Soil drying, however, strongly suppresses growth in methane (CH _4 ) emissions. Considering the global warming potential (GWP) of CO _2 and CH _4 emissions together, soil drying weakens the CLM projected GWP associated with carbon fluxes from the permafrost zone by more than 50% compared to a non-drying case. This high sensitivity to hydrologic change highlights the need for better understanding and modeling of landscape-scale changes in soil moisture conditions in response to permafrost thaw in order to more accurately assess the potential magnitude of the permafrost carbon-climate feedback.

Analyses of remote sensing data, surface observations and output from a regional atmosphere model point to new records in 2010 for surface melt and albedo, runoff, the number of days when bare ice is exposed and surface mass balance of the Greenland ice sheet, especially over its west and southwest regions. Early melt onset in spring, triggered by above-normal near-surface air temperatures, contributed to accelerated snowpack metamorphism and premature bare ice exposure, rapidly reducing the surface albedo. Warm conditions persisted through summer, with the positive albedo feedback mechanism being a major contributor to large negative surface mass balance anomalies. Summer snowfall was below average. This helped to maintain low albedo through the 2010 melting season, which also lasted longer than usual.

Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land–atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.

Studies have reported important effects of recent climate change on Antarctic species, but there has been to our knowledge no attempt to explicitly link those results to forecasted population responses to climate change. Antarctic sea ice extent (SIE) is projected to shrink as concentrations of atmospheric greenhouse gases (GHGs) increase, and emperor penguins (Aptenodytes forsteri) are extremely sensitive to these changes because they use sea ice as a breeding, foraging and molting habitat. We project emperor penguin population responses to future sea ice changes, using a stochastic population model that combines a unique long-term demographic dataset (1962-2005) from a colony in Terre Adélie, Antarctica and projections of SIE from General Circulation Models (GCM) of Earth's climate included in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment report. We show that the increased frequency of warm events associated with projected decreases in SIE will reduce the population viability. The probability of quasi-extinction (a decline of 95% or more) is at least 36% by 2100. The median population size is projected to decline from approximately 6,000 to approximately 400 breeding pairs over this period. To avoid extinction, emperor penguins will have to adapt, migrate or change the timing of their growth stages. However, given the future projected increases in GHGs and its effect on Antarctic climate, evolution or migration seem unlikely for such long lived species at the remote southern end of the Earth.

Across High Asia, the amount, timing, and spatial patterns of snow and ice melt play key roles in providing water for downstream irrigation, hydropower generation, and general consumption. The goal of this paper is to distinguish the specific contribution of seasonal snow versus glacier ice melt in the major basins of High Mountain Asia: Ganges, Brahmaputra, Indus, Amu Darya, and Syr Darya. Our methodology involves the application of MODIS-derived remote sensing products to separately calculate daily melt outputs from snow and glacier ice. Using an automated partitioning method, we generate daily maps of (1) snow over glacier ice, (2) exposed glacier ice, and (3) snow over land. These are inputs to a temperature index model that yields melt water volumes contributing to river flow. Results for the five major High Mountain Asia basins show that the western regions are heavily reliant on snow and ice melt sources for summer dry season flow when demand is at a peak, whereas monsoon rainfall dominates runoff during the summer period in the east. While uncertainty remains in the temperature index model applied here, our approach to partitioning melt from seasonal snow and glacier ice is both innovative and systematic and more constrained than previous efforts with similar goals.

Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction.

Here we present the radiative and snowmelt impacts of dust deposition to snow cover using a 6‐year energy balance record (2005–2010) at alpine and subalpine micrometeorological towers in the Senator Beck Basin Study Area (SBBSA) in southwestern Colorado, USA. These results follow from the measurements described in part I. We simulate the evolution of snow water equivalent at each station under scenarios of observed and dust‐free conditions, and +2°C and +4°C melt‐season temperature perturbations to these scenarios. Over the 6 years of record, daily mean dust radiative forcing ranged from 0 to 214 W m −2 , with hourly peaks up to 409 W m −2 . Mean springtime dust radiative forcings across the period ranged from 31 to 49 W m −2 at the alpine site and 45 to 75 W m −2 at the subalpine site, in turn shortening snow cover duration by 21 to 51 days. The dust‐advanced loss of snow cover (days) is linearly related to total dust concentration at the end of snow cover, despite temporal variability in dust exposure and solar irradiance. Under clean snow conditions, the temperature increases shorten snow cover by 5–18 days, whereas in the presence of dust they only shorten snow duration by 0–6 days. Dust radiative forcing also causes faster and earlier peak snowmelt outflow with daily mean snowpack outflow doubling under the heaviest dust conditions. On average, snow cover at the towers is lost 2.5 days after peak outflow in dusty conditions, and 1–2 weeks after peak outflow in clean conditions.

Abstract Rapid warming of the Antarctic Peninsula over the past several decades has led to extensive surface melting on its eastern side, and the disintegration of the Prince Gustav, Larsen A, and Larsen B ice shelves. The warming trend has been attributed to strengthening of circumpolar westerlies resulting from a positive trend in the Southern Annular Mode (SAM), which is thought to promote more frequent warm, dry, downsloping foehn winds along the lee, or eastern side, of the peninsula. We examined variability in foehn frequency and its relationship to temperature and patterns of synoptic‐scale circulation using a multidecadal meteorological record from the Argentine station Matienzo, located between the Larsen A and B embayments. This record was further augmented with a network of six weather stations installed under the U.S. NSF LARsen Ice Shelf System, Antarctica, project. Significant warming was observed in all seasons at Matienzo, with the largest seasonal increase occurring in austral winter (+3.71°C between 1962–1972 and 1999–2010). Frequency and duration of foehn events were found to strongly influence regional temperature variability over hourly to seasonal time scales. Surface temperature and foehn winds were also sensitive to climate variability, with both variables exhibiting strong, positive correlations with the SAM index. Concomitant positive trends in foehn frequency, temperature, and SAM are present during austral summer, with sustained foehn events consistently associated with surface melting across the ice sheet and ice shelves. These observations support the notion that increased foehn frequency played a critical role in precipitating the collapse of the Larsen B ice shelf.

Abstract We use laser altimetry from the Ice, Cloud, and land Elevation Satellite (ICESat) to map the grounding zone (GZ) of the Ross Ice Shelf, Antarctica, at 491 locations where ICESat tracks cross the grounding line (GL). Ice flexure in the GZ occurs as the ice shelf responds to short-term sea-level changes due primarily to tides. ICESat repeat-track analysis can be used to detect this region of flexure since each repeated pass is acquired at a different tidal phase; the technique provides estimates for both the landward limit of flexure and the point where the ice becomes hydrostatically balanced. We find that the ICESat-derived landward limits of tidal flexure are, in many places, offset by several km (and up to ∼60km) from the GL mapped previously using other satellite methods. We discuss the reasons why different mapping methods lead to different GL estimates, including: instrument limitations; variability in the surface topographic structure of the GZ; and the presence of ice plains. We conclude that reliable and accurate mapping of the GL is most likely to be achieved when based on synthesis of several satellite datasets.

Abstract In Earth system models, terrestrial snow is usually modeled by the land surface component. In most cases, these snow models have been developed with an emphasis on seasonal snow. Questions about future sea level rise, however, prompt the need for a realistic representation of perennial snow, as snow processes play a key role in the mass balance of glaciers and ice sheets. Here we enhance realism of modeled polar snow in the Community Land Model (CLM), the land component of the Community Earth System Model (CESM), by implementing (1) new parametrizations for fresh snow density, destructive metamorphism, and compaction by overburden pressure, (2) by allowing for deeper snow packs, and (3) by introducing drifting snow compaction, with a focus on the ice sheet interior. Comparison with Greenlandic and Antarctic snow density observations show that the new physics improve model skill in predicting firn and near‐surface density in the absence of melt. Moreover, compensating biases are removed and spurious subsurface melt rates at ice sheets are eliminated. The deeper snow pack enhances refreezing and allows for deeper percolation, raising ice temperatures up to 15°C above the skin temperature.

Abstract Understanding the sensitivity of groundwater generation to climate in a mountain system is complicated by the tight coupling of snow dynamics to vegetation and topography. To address these feedbacks, we combine light detection and ranging (LiDAR)‐derived snow observations with an integrated hydrologic model to quantify spatially and temporally distributed water fluxes across varying climate conditions in a Colorado River headwater basin. Results indicate that annual groundwater flow is an important and stable source of stream water. However, interflow decreases during drought as a function increased plant water use and the relative fraction of groundwater to streams increases. Seasonal snowmelt and vegetation water use regulate small recharge rates in the lower portions of the basin, but snowmelt transported via interflow from high mountain ridges toward convergent topographic zones defines preferential recharge in the upper subalpine. Recharge in this zone appears decoupled from annual climate variability and resilient to drought.

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.