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Olivier Champagne
,
Olga Zolina
,
Jean-Pierre Dedieu
,
Mareile Wolff
, and
Hans-Werner Jacobi

Abstract

The Svalbard archipelago, in the Atlantic–Arctic region, has been affected by a strong increase in precipitation in the last decades, with major potential impacts for the cryosphere, biogeochemical cycles, and the ecosystems. Ny-Ålesund (79°N), in the northwest part of Svalbard, hosts invaluable meteorological records widely used by many researchers. Among the observed parameters, the amount of precipitation is subject to large biases, mainly due to the well-known precipitation gauges undercatch during windy conditions. The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. We applied several correction factors developed in the last decades, based on local wind speed and temperature. We forced these corrections with 12-hourly precipitation data from the Ny-Ålesund weather station. Taking the period 1975–2022, the trend of precipitation increased from 3.8 mm yr−1 in the observations to 4.5 mm yr−1 (±0.2) after the corrections, mainly due to enhanced snowfall in November–January months. Taking the most recent 40-yr period (1983–2022), the amount of precipitation still increased by 3.8 mm yr−1 in the observations, but only by 2.6 mm yr−1 (±0.5) after the corrections. The recent observed trend of precipitation stays large due to an increase of wet snowfall and rainfall that are measured more efficiently by the precipitation gauge. This result shows the need of applying correction factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

Significance Statement

The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975–2022 period was real and how it was impacted by the gauge undercatch. The results show that the observed trend of precipitation was overestimated when calculated in the most recent 40-yr period (1983–2022). This overestimation was large due to an increase with time of wet snowfall and rainfall that were measured more efficiently by the precipitation gauge. This result shows the need of applying corrections factors when using precipitation gauge data, especially in regions exhibiting large interannual changes of weather conditions.

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Free access
Clément Guilloteau
and
Efi Foufoula-Georgiou

Abstract

Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

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Savannah K. Jorgensen
and
John W. Nielsen-Gammon

Abstract

This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here.

Significance Statement

Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.

Open access
J. R. Levey
and
A. Sankarasubramanian

Abstract

Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

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Yanjuan Wu
,
Ivan D. Haigh
,
Chao Gao
,
Luke J. Jenkins
,
Joshua Green
,
Robert Jane
,
Yu Xu
,
Hengzhi Hu
, and
Naicheng Wu

Abstract

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China’s Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall’s rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions.

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G. Cristina Recalde-Coronel
,
Benjamin Zaitchik
,
William K. Pan
,
Yifan Zhou
, and
Hamada Badr

Abstract

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March–May) using ensemble hindcasts for 2002–17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño–Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Open access
Xinxin Xie
,
Xiao Xiao
,
Jieying He
,
Pablo Saavedra Garfias
,
Tiejian Li
,
Xiaoyu Yu
,
Songyan Gu
, and
Yang Guo

Abstract

This study investigates precipitation observed by a set of collocated ground-based instruments in Zhuhai, a coastal city located at the southern tip of the Pearl River Delta of Guangdong Province in South China. Seven months of ground-based observations from a tipping-bucket rain gauge (RG), two laser disdrometers (PARSIVEL and PWS), and a vertically-pointing Doppler Micro Rain Radar-2 (MRR), spanning from December 2021 to July 2022, are statistically evaluated to provide a reliable reference for China’s spaceborne precipitation measurement mission. Rainfall measurement discrepancies are found between the instruments, though the collocated deployment mitigates uncertainties originating from spatial/temporal variabilities of precipitation. The RG underestimates hourly rain amounts at the observation site, opposite to previous studies, leading to 18.2% percent bias (Pbias) of hourly rain amounts when compared to the PARSIVEL. With the same measurement principle, the hourly-accumulated rain between the two laser disdrometers has a Pbias of 15.3%. Discrepancies between MRR and disdrometers are assumed to be due to different temporal/spatial resolution, instrument sensitivities and observation geometry, with a Pbias of mass-weighted mean diameter and normalized intercept parameter of gamma size distribution less than 9%. The vertical profiles of drop size distribution (DSD) derived from the MRR are further examined during extreme rainfalls in the East Asia monsoon season (May, June, and July). Attributed to the abundant moisture which favors the growth of raindrops, coalescence is identified as the predominant effective process and the raindrop mass-weighted mean diameter increases by 33.7% when falling from 2000 m to 600 m during the extreme precipitation event in May.

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Francisca Aguirre-Correa
,
Jordi Vilà-Guerau de Arellano
,
Reinder Ronda
,
Felipe Lobos-Roco
,
Francisco Suárez
, and
Oscar Hartogensis

Abstract

Observations over a salt-water lagoon in the Altiplano show that evaporation (E) is triggered at noon, concurrent to the transition of a shallow, stable atmospheric boundary layer (ABL) into a deep mixed layer. We investigate the coupling between the ABL and E drivers using a land-atmosphere conceptual model, observations and a regional model. Additionally, we analyze the ABL interaction with the aerodynamic and radiative components of evaporation using the Penman equation adapted to salt-water. Our results demonstrate that non-local processes are dominant in driving E. In the morning the ABL is controlled by the local advection of warm air (∼5 Kh−1), which results in a shallow (<350 m), stable ABL, with virtually no mixing and no E (<50 Wm−2). The warm-air advection ultimately connects the ABL with the residual layer above, increasing the ABL height (h) by ∼1-km. At midday a thermally-driven regional flow arrives to the lagoon, which first advects a deeper ABL from the surrounding desert (∼1500 mh−1) that leads to an extra ∼700-m h increase. The regional flow also causes an increase in wind (∼12 ms−1) and an ABL collapse due to the entrance of cold air (∼−2 Kh−1) with a shallower ABL (∼−350 mh−1). The turbulence produced by the wind decreases the aerodynamic resistance and mixes the water body releasing the energy previously stored in the lake. The ABL feedback on E through vapor pressure enables high evaporation values (∼450 Wm−2 at 1430 LT). These results contribute to the understanding of E of water bodies in semi-arid conditions and emphasize the importance of understanding ABL processes when describing evaporation drivers.

Open access
Daniel Whitesel
,
Rezaul Mahmood
,
Christopher Phillips
,
Joshua Roundy
,
Eric Rappin
,
Paul Flanagan
,
Joseph A. Santanello Jr.
,
Udaysankar Nair
, and
Roger Pielke Sr.

Abstract

Land use land cover change affects weather and climate. This paper quantifies land-atmosphere interactions over irrigated and non-irrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify some land-atmosphere interactions as it relates to convection. They include: the Convective Triggering Potential (CTP) and Low-Level Humidity Index (HIlow), and the Lifting Condensation Level (LCL) Deficit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISS) for Rogers Farm and York Airport along with soundings launched from the Doppler on Wheels (DOW) sites. Each metric was categorized by Intensive Observation Period (IOP), cloud cover, and time of day.

Results show that with higher CTP, lower HIlow, and lower LCL Deficit, conditions were more favorable for convective development over irrigated land use. When metrics were grouped and analyzed by IOP, compared to non-irrigated land use, HIlow was found to be lower for irrigated land use suggesting favorable conditions for convective development. Furthermore, when metrics were grouped and analyzed by clear and non-clear days, CTP values were higher over irrigated cropland compared to non-irrigated land use. In addition, compared to non-irrigated land use, LCL Deficit during the peak growing season was lower over irrigated land use, suggesting favorable condition for convection. It is found that with the transition from the early summer to the mid/peak summer and increased irrigation, the environment became more favorable for convective development over irrigated land use. Finally, it was found that regardless of background atmospheric conditions, irrigated land use provided a favorable environment for convective development.

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