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Maheshwari Neelam, Rajat Bindlish, Peggy O’Neill, George J. Huffman, Rolf Reichle, Steven Chan, and Andreas Colliander

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).

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D.J. Mullan, I.D. Barr, R.P. Flood, J.M. Galloway, A.M.W. Newton, and G.T. Swindles

Abstract

Winter roads play a vital role in linking communities and building economies in the northern high latitudes. With these regions warming two to three times faster than the global average, climate change threatens the long-term viability of these important seasonal transport routes. We examine how climate change will impact the world’s busiest heavy-haul winter road – the Tibbitt to Contwoyto Winter Road (TCWR) in northern Canada. The FLake freshwater lake model is used to project ice thickness for a lake at the start of the TCWR – first using observational climate data, and second using modelled future climate scenarios corresponding to varying rates of warming ranging from 1.5°C to 4°C above preindustrial temperatures. Our results suggest that 2°C warming could be a tipping point for the viability of the TCWR, requiring at best costly adaptation and at worst alternative forms of transportation. Containing warming to the more ambitious temperature target of 1.5°C pledged at the 2016 Paris Agreement may be the only way to keep the TCWR viable – albeit with a shortened annual operational season relative to present. More widely, we show that higher regional winter warming across much of the rest of Arctic North America threatens the long-term viability of winter roads at a continental scale. This underlines the importance of continued global efforts to curb greenhouse gas emissions to avoid many long-term and irreversible impacts of climate change.

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Robbie Iacovazzi, Quanhua “Mark” Liu, and Changyong Cao

CAPSULE SUMMARY

2020 Community Meeting on NOAA Satellites: Informing the Future of NOAA Satellite Observations

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Shin-Young Park and Cheol-Hee Kim

Abstract

Precipitation susceptibility (So), a parameter of aerosol-cloud-precipitation interaction over Northeast Asia during the Korea-United States Air Quality (KORUS-AQ) campaign, was analyzed using the CLAVR-x satellite data and WRF-Chem model. As Northeast Asia is one of the areas with the highest aerosol emissions, this study is expected to explore more elaborate aerosol-cloud linkages.

Our results obtained from satellite data showed that So increased as the atmospheric condition became stable and humid, and the shift of the water conversion process to precipitation occurred in the LWP range of 300–500 g m-2. The So exhibited a maximum value of 0.61 at an LWP of 350 g m-2 where the dominance of the cloud-water conversion process changed from autoconversion to accretion. In the aerosol–cloud relation, the susceptibility of the cloud-drop effective radius showed a positive response to the cloud droplet number concentration (Nd) regardless of the environmental conditions, whereas the LWP vs. Nd relationship was highly dependent on the meteorological conditions.

The WRF-Chem produced higher So values than those of the satellite data by factors of 2.4–3.3; the simulated results exhibited differences in shape, range, and amplitude. The overestimation of So was mainly due to the high precipitation rate under low LWP conditions as compared to the satellite observations. This result is associated with the initiation and intensity of precipitation, considering both autoconversion and accretion. Our modeling results were verified during KORUS-AQ, which implied that the aerosol–cloud relationship might be elucidated by improved microphysical parameterization schemes based on more detailed measurements such as aircraft-based observations.

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Hui Li, Alexey Fedorov, and Wei Liu

Abstract

This study compares the impacts of Arctic sea ice decline on the Atlantic Meridional Overturning Circulation (AMOC) in two configurations of the Community Earth System Model (CESM) with different horizontal resolution. In a suite of model experiments we impose radiative imbalance at the ice surface, replicating a loss of sea ice cover comparable to the observed during 1979-2014, and find dramatic differences in the AMOC response between the two models. In the lower-resolution configuration, the AMOC weakens by about one third over the first 100 years, approaching a new quasi-equilibrium. By contrast, in the higher-resolution configuration, the AMOC weakens by ~10% during the first 20-30 years followed by a full recovery driven by invigorated deep water formation in the Labrador Sea and adjacent regions. We investigate these differences using a diagnostic AMOC stability indicator, which reflects the AMOC freshwater transport in and out of the basin and hence the strength of the basin-scale salt-advection feedback. This indicator suggests that the AMOC in the lower-resolution model is less stable and more sensitive to surface perturbations, as confirmed by hosing experiments mimicking Arctic freshening due to sea ice decline. Differences between the models’ mean states, including the Atlantic mean surface freshwater fluxes, control the differences in AMOC stability. Our results demonstrate that the AMOC stability indicator is indeed useful for evaluating AMOC sensitivity to perturbations. Finally, we emphasize that, despite the differences in the long-term adjustment, both models simulate a multi-decadal AMOC weakening caused by Arctic sea ice decline, relevant to climate change.

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Matthew T. Bray, David D. Turner, and Gijs de Boer

Abstract

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary-layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary-layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

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Xin Li, Xiaolei Zou, Mingjian Zeng, Ning Wang, and Fei Tang

Abstract

Aimed at improving all-sky Cross-track Infrared Sounder (CrIS) radiance assimilation, this study explores the benefits for CrIS all-sky radiance simulations, focusing on the accuracy of background cloud information, through assimilating cloud liquid water path (LWP), ice water path (IWP), and rain water path (RWP) data retrieved from the Advanced Technology Microwave Sounder (ATMS). The Community Radiative Transfer Model (CRTM), which considers cloud scattering and absorption processes, is applied to simulate CrIS radiances. The Gridpoint Statistical Interpolation ensemble-variational data assimilation (DA) is updated by incorporating ensemble covariances of hydrometeor variables and observation operators of LWP, IWP, and RWP. First, two DA experiments named DActrl and DAcwp are conducted with (DAcwp) and without (DActrl) assimilating ATMS LWP, IWP, and RWP data. Assimilating ATMS cloud retrieval data results in better spatial distributions of hydrometers for both a Meiyu rainfall case and a typhoon case. Analyses of DActrl and DAcwp are then used as input to the CRTM to generate CrIS all-sky radiance simulations SMallsky_DActrl and SMallsky_DAcwp, respectively. Improvements in the DAcwp analyses of hydrometeor variables are found to benefit CrIS radiance simulations, especially in cloudy regions. A long period of statistics reveals that the biases and standard deviations of all-sky observations minus simulations from SMallsky_DAcwp are notably smaller than those from SMallsky_DActrl. This pilot study suggests the potential benefit of combining the use of microwave cloud retrieval products for all-sky infrared DA.

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Thomas W. N. Haine, Renske Gelderloos, Miguel A. Jimenez-Urias, Ali H. Siddiqui, Gerard Lemson, Dimitri Medvedev, Alex Szalay, Ryan P. Abernathey, Mattia Almansi, and Christopher N. Hill

Abstract

Computational Oceanography is the study of ocean phenomena by numerical simulation, especially dynamical and physical phenomena. Progress in information technology has driven exponential growth in the number of global ocean observations and the fidelity of numerical simulations of the ocean in the past few decades. The growth has been exponentially faster for ocean simulations, however. We argue that this faster growth is shifting the importance of field measurements and numerical simulations for oceanographic research. It is leading to the maturation of Computational Oceanography as a branch of marine science on par with observational oceanography. One implication is that ultra-resolved ocean simulations are only loosely constrained by observations. Another implication is that barriers to analyzing the output of such simulations should be removed. Although some specific limits and challenges exist, many opportunities are identified for the future of Computational Oceanography. Most important is the prospect of hybrid computational and observational approaches to advance understanding of the ocean.

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Tristan S. L’Ecuyer, Brian J. Drouin, James Anheuser, Meredith Grames, David Henderson, Xianglei Huang, Brian H. Kahn, Jennifer E. Kay, Boon H. Lim, Marian Mateling, Aronne Merrelli, Nathaniel B. Miller, Sharmila Padmanabhan, Colten Peterson, Nicole-Jeanne Schlegel, Mary L. White, and Yan Xie

Abstract

The Earth’s climate is strongly influenced by energy deficits at the poles that emit more thermal energy than they receive from the sun. Energy exchanges between the surface and atmosphere influence the local environment while heat transport from lower latitudes drives midlatitude atmospheric and oceanic circulations. In the Arctic, in particular, local energy imbalances induce strong seasonality in surface-atmosphere heat exchanges and an acute sensitivity to forced climate variations. Despite these important local and global influences, the largest contributions to the polar atmospheric and surface energy budgets have not been fully characterized. The spectral variation of far-infrared radiation that makes up 60% of polar thermal emission has never been systematically measured impeding progress toward consensus in predicted rates of Arctic warming, sea ice decline, and ice sheet melt.

Enabled by recent advances in sensor miniaturization and CubeSat technology, the Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) mission will document, for the first time, the spectral, spatial, and temporal variations of polar far-infrared emission. Selected under NASA’s Earth Ventures Instrument (EVI) program, PREFIRE will utilize new light weight, low-power, ambient temperature detectors capable of measuring at wavelengths up to 50 micrometers to quantify Earth’s far-infrared spectrum. Estimates of spectral surface emissivity, water vapor, cloud properties, and the atmospheric greenhouse effect derived from these measurements offer the potential to advance our understanding of the factors that modulate thermal fluxes in the cold, dry conditions characteristic of the polar regions.

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Radan Huth and Martin Dubrovský

Abstract

Studies detecting trends in climate elements typically concentrate on their local significance, ignoring the question on whether the significant local trends may or may not have occurred due to chance. The present paper fills this gap by examining several approaches to detecting statistical significance of trends defined on a grid, that is on a regional scale. To this end, we introduce a novel simple procedure of significance testing, which is based on counting signs of local trends (sign test), and compare it with five other approaches to testing collective significance of trends (counting, extended Mann-Kendall, Walker, fdr, and regression tests). Synthetic data are used to construct null distributions of trend statistics, to determine critical values of the tests, and to assess the performance of tests in terms of type II error. For lower values of spatial and temporal autocorrelations, the sign test and extended Mann-Kendall test perform slightly better than the counting test; these three tests outperform Walker, fdr, and regression tests by quite a wide margin. For high autocorrelations, which is a more realistic case, all tests become similar in their performance, with the exception of the regression test, which performs somewhat worse. Some tests cannot be used under specific conditions because of their construction: Walker and fdr tests for high temporal autocorrelations; sign test under high spatial autocorrelations.

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