Impact of Daily Arctic Sea Ice Variability in CAM3.0 during Fall and Winter

Climate projections suggest that an ice-free summer Arctic Ocean is possible within several decades and with this comes the prospect of increased ship traffic and safety concerns. The daily sea ice concentration tendency in five Coupled Model Intercomparison Project phase 5 (CMIP5) simulations is compared with observations to reveal that many models underestimate this quantity that describes high-frequency ice movements, particularly in the marginal ice zone. To investigate whether high-frequency ice variability impacts the atmosphere, the Community Atmosphere Model, version 3.0 (CAM3.0), is forced by sea ice with and without daily fluctuations. Two 100-member ensemble experiments with daily varying (DAILY) and smoothly varying (SMTH) sea ice are conducted, along with a climatological control, for an anoma- lously low ice period (August 2006–November 2007). Results are presented for three periods: September 2006, October 2006, and December–February (DJF) 2006/07. The atmospheric response differs between DAILY and SMTH. In September, sea ice differences lead to an anomalous high and weaker storm activity over northern Europe. During October, the ice expands equatorward faster in DAILY than SMTH in the Siberian seas and leads to a local response of near-surface cooling. In DJF, there is a 1.5-hPa positive sea level pressure anomaly over North America, leading to anomalous northerly flow and anomalously cool continental U.S. temperatures. While the atmospheric responses are modest, the differences arising from high temporal frequency ice variability cannot be ignored. Increasing the accuracy of coupled model sea ice variations on short time scales is needed to improve short-term coupled model forecasts

The potential impact of climate change on the efficiency and reliability of solar, hydro, and wind energy sources

Climate change impacts the electric power system by affecting both the load and generation. It is paramount to understand this impact in the context of renewable energy as their market share has increased and will continue to grow. This study investigates the impact of climate change on the supply of renewable energy through applying novel metrics of intermittency, power production and storage required by the renewable energy plants as a function of historical climate data variability. Here we focus on and compare two disparate locations, Palma de Mallorca in the Balearic Islands and Cordova, Alaska. The main results of this analysis of wind, solar radiation and precipitation over the 1950–2020 period show that climate change impacts both the total supply available and its variability. Importantly, this impact is found to vary significantly with location. This analysis demonstrates the feasibility of a process to evaluate the local optimal mix of renewables, the changing needs for energy storage as well as the ability to evaluate the impact on grid reliability regarding both penetration of the increasing renewable resources and changes in the variability of the resource. This framework can be used to quantify the impact on both transmission grids and microgrids and can guide possible mitigation paths.P.C. and D.G. acknowledge financial support from Ministerio de Ciencia e Innovación (Spain), the Agencia Estatal de Investigación (AEI, Spain), and the Fondo Europeo de Desarrollo Regional (FEDER, EU) under grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711). D.N. gratefully acknowledges support from DOE Project GMLC 1.5.02—Resilient Alaskan Distribution system Improvements using Automation, Network analysis, Control, and Energy storage (RADIANCE). U.S.B. acknowledges support from the National Science Foundation under award #OIA-1753748 and by the State of Alaska for material which this work is based upon

Historical Climatology of the Alaska Climate Divisions

Complex topography and proximity to coasts results in multiple climate types in Alaska. Climate variability is regional in Alaska. Understanding regional climate variability can further evaluation of climate change, seasonal climate prediction, and teleconnection impacts. Novel climate divisions for Alaska present new avenues for climate products and services.NOAA Climate Program Office grant NA10OAR4310055 through CIFA

Dynamics of Aboveground Phytomass of the Circumpolar Arctic Tundra During the Past Three Decades

Numerous studies have evaluated the dynamics of Arctic tundra vegetation throughout the past few decades, using remotely sensed proxies of vegetation, such as the normalized difference vegetation index (NDVI). While extremely useful, these coarse-scale satellite-derived measurements give us minimal information with regard to how these changes are being expressed on the ground, in terms of tundra structure and function. In this analysis, we used a strong regression model between NDVI and aboveground tundra phytomass, developed from extensive field-harvested measurements of vegetation biomass, to estimate the biomass dynamics of the circumpolar Arctic tundra over the period of continuous satellite records (1982-2010). We found that the southernmost tundra subzones (C-E) dominate the increases in biomass, ranging from 20 to 26%, although there was a high degree of heterogeneity across regions, floristic provinces, and vegetation types. The estimated increase in carbon of the aboveground live vegetation of 0.40 Pg C over the past three decades is substantial, although quite small relative to anthropogenic C emissions. However, a 19.8% average increase in aboveground biomass has major implications for nearly all aspects of tundra ecosystems including hydrology, active layer depths, permafrost regimes, wildlife and human use of Arctic landscapes. While spatially extensive on-the-ground measurements of tundra biomass were conducted in the development of this analysis, validation is still impossible without more repeated, long-term monitoring of Arctic tundra biomass in the field

Fluctuating Atlantic inflows modulate Arctic atlantification

Enhanced warm, salty subarctic inflows drive high-latitude atlantification, which weakens oceanic stratification, amplifies heat fluxes, and reduces sea ice. In this work, we show that the atmospheric Arctic Dipole (AD) associated with anticyclonic winds over North America and cyclonic winds over Eurasia modulates inflows from the North Atlantic across the Nordic Seas. The alternating AD phases create a “switchgear mechanism.” From 2007 to 2021, this switchgear mechanism weakened northward inflows and enhanced sea-ice export across Fram Strait and increased inflows throughout the Barents Sea. By favoring stronger Arctic Ocean circulation, transferring freshwater into the Amerasian Basin, boosting stratification, and lowering oceanic heat fluxes there after 2007, AD+ contributed to slowing sea-ice loss. A transition to an AD− phase may accelerate the Arctic sea-ice decline, which would further change the Arctic climate system.acceptedVersio

Climate Divisions for Alaska Based on Objective Methods

Alaska encompasses several climate types because of its vast size, high-latitude location, proximity to oceans, and complex topography. There is a great need to understand how climate varies regionally for climatic research and forecasting applications. Although climate-type zones have been established for Alaska on the basis of seasonal climatological mean behavior, there has been little attempt to construct climate divisions that identify regions with consistently homogeneous climatic variability. In this study, cluster analysis was applied to monthly-average temperature data from 1977 to 2010 at a robust set of weather stations to develop climate divisions for the state. Mean-adjusted Advanced Very High Resolution Radiometer surface temperature estimates were employed to fill in missing temperature data when possible. Thirteen climate divisions were identified on the basis of the cluster analysis and were subsequently refined using local expert knowledge. Divisional boundary lines were drawn that encompass the grouped stations by following major surrounding topographic boundaries. Correlation analysis between station and gridded downscaled temperature and precipitation data supported the division placement and boundaries. The new divisions north of the Alaska Range were the North Slope, West Coast, Central Interior, Northeast Interior, and Northwest Interior. Divisions south of the Alaska Range were Cook Inlet, Bristol Bay, Aleutians, Northeast Gulf, Northwest Gulf, North Panhandle, Central Panhandle, and South Panhandle. Correlations with various Pacific Ocean and Arctic climatic teleconnection indices showed numerous significant relationships between seasonal division average temperature and the Arctic Oscillation, Pacific–North American pattern, North Pacific index, and Pacific decadal oscillation.Keywords: Statistical techniques, Climate variability, Climate classification/regimes, Regional effects, Climatolog