ウィスコンシン ダイガク マディソンコウ ガ ジッシ シテイル ナンキョク ムジン キショウ カンソク (AWS) ケイカク ノ 2011-2012 ネン カキ ノ カツドウ

ウィスコンシン大学マディソン校で推進している南極無人気象観測計画(Antarctic Automatic Weather Station(AWS)program)の32 年目の観測が,2011/2012年の南半球夏期に完了した.無人気象観測網を利用して南極の気象と気候の研究が行われている.今シーズンはロス島周辺域,ロス棚氷,西南極,東南極にわたる領域で活動した.基本的に観測点のデータはアルゴス衛星を中継して配信されるが,今年はロス島周辺域の多くの観測点で,マクマード基地を中継して"Freewave modem"を通して配信された.各無人気象観測点報告には,現在設置されている測器と動作状況が含まれる.また,無人気象観測計画の全体像を,野外活動の実施状況に沿って示す.During the 2011-2012 austral summer, the Antarctic Automatic Weather Station (AWS) program at the University of Wisconsin?Madison completed its 32nd year of observations. Ongoing studies utilizing the network include topics in Antarctic meteorology and climate studies. This field season consisted of work throughout the Ross Island area, the Ross Ice Shelf, West Antarctica, and East Antarctica. Argos satellite transmissions are the primary method for relaying station data, but throughout this year, a number of stations in the Ross Island area have been converted to Freewave modems, with their data being relayed through McMurdo station. Each AWS station report contains information regarding the instrumentation currently installed and the work performed at each site. An overview of the AWS applications is included along with field work accomplished

Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations

In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this &ldquo;new&rdquo; Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative &ldquo;perennial&rdquo; and &ldquo;seasonal&rdquo; time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability. </div

Indian summer monsoon during the last two millennia

The monsoon is a large-scale feature of the tropical atmospheric circulation, affecting people and economies in the world's most densely populated regions. Future trends due to natural variability and human-induced climate changes are uncertain. Palaeoclimate records can improve our understanding of monsoon dynamics and thereby reduce this uncertainty. Palaeoclimate records have revealed a dramatic decrease in the Asian summer monsoon since the early Holocene maximum 9 ka BP. Here we focus on the last 2 ka, where some records indicate an increasing trend in the summer monsoon. Analysing Globigerina bulloides upwelling records from the Arabian Sea, we find the weakest monsoon occurred 1500 a BP, with an increasing trend towards the present

Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations

The article of record as published may be found at https://doi.org/10.5194/gmd-11-4817-2018Includes supplementary materialThe Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP).This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP)

Thicker Clouds and Accelerated Arctic Sea Ice Decline: The Atmosphere‐Sea Ice Interactions in Spring

Observations show that increased Arctic cloud cover in the spring is linked with sea ice decline. As the atmosphere and sea ice can influence each other, which one plays the leading role in spring remains unclear. Here we demonstrate, through observational data diagnosis and numerical modeling, that there is active coupling between the atmosphere and sea ice in early spring. Sea ice melting and thus the presence of more open water lead to stronger evaporation and promote cloud formation that increases downward longwave flux, leading to even more ice melt. Spring clouds are a driving force in the disappearance of sea ice and displacing the mechanism of atmosphere-sea ice coupling from April to June. These results suggest the need to accurately model interactions of Arctic clouds and radiation in Earth System Models in order to improve projections of the future of the Arctic.NASA Earth and Space Science Fellowship program [80NSSC18K1339]; NASA CERES project through the University of Arizona [80NSSC19K0172]; National Center for Atmospheric Research (NCAR) - National Science Foundation (NSF) [1852977]; NASA [15-CCST15-0025]; NSF [AGS-1354402, AGS-1445956]; National Oceanic and Atmospheric Administration [NA16NWS4680013]; National Science Foundation6 month embargo; published online: 19 June 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Automatic Weather Station (AWS) Program operated by the University of Wisconsin-Madison during the 2011-2012 field season

During the 2011-2012 austral summer, the Antarctic Automatic Weather Station (AWS) program at the University of Wisconsin?Madison completed its 32nd year of observations. Ongoing studies utilizing the network include topics in Antarctic meteorology and climate studies. This field season consisted of work throughout the Ross Island area, the Ross Ice Shelf, West Antarctica, and East Antarctica. Argos satellite transmissions are the primary method for relaying station data, but throughout this year, a number of stations in the Ross Island area have been converted to Freewave modems, with their data being relayed through McMurdo station. Each AWS station report contains information regarding the instrumentation currently installed and the work performed at each site. An overview of the AWS applications is included along with field work accomplished

Less Surface Sea Ice Melt in the CESM2 Improves Arctic Sea Ice Simulation With Minimal Non-Polar Climate Impacts

This study isolates the influence of sea ice mean state on pre-industrial climate and transient 1850-2100 climate change within a fully coupled global model: The Community Earth System Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover. Importantly, increased Arctic sea ice in the modified model reduces a present-day late-summer ice cover bias. Of interest to coupled model development, this bias reduction is realized without degrading the global simulation including top-of-atmosphere energy imbalance, surface temperature, surface precipitation, and major modes of climate variability. The influence of these sea ice physics changes on transient 1850-2100 climate change is compared within a large initial condition ensemble framework. Despite similar global warming, the modified model with thicker Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free Arctic Ocean. Differences in transient climate change between the modified model and CESM2 are challenging to detect due to large internally generated climate variability. In particular, two common sea ice benchmarks-sea ice sensitivity and sea ice trends-are of limited value for comparing models with similar global warming. More broadly, these results show the importance of a reasonable Arctic sea ice mean state when simulating the transition to an ice-free Arctic Ocean in a warming world. Additionally, this work highlights the importance of large initial condition ensembles for credible model-to-model and observation-model comparisons.11Ysciescopu