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Atmosphere
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Recent Progress and Future Prospects of Subseasonal and Seasonal Climate...
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Recent Progress and Future Prospects of Subseasonal and Seasonal Climate Prediction

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (21 August 2021) | Viewed by 6609

Special Issue Editors

Dr. Suryun Ham

E-Mail Website
Guest Editor
APEC Climate Center, Busan 48058, Korea
Interests: seasonal climate modeling; air-sea coupled modeling; subseasonal prediction; East Asia Monsoon; precipitation processes
Dr. Hiroyuki Murakami

E-Mail Website
Guest Editor
Seasonal-to-Decadal Variability and Predictability Division, Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ 08540, USA
Interests: tropical cyclones; climate dynamics; future projections; predictions and predictability; numerical modelling; extreme events
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, sub-seasonal to seasonal (S2S) prediction has become more important with the extreme climate. At the same time, the demand for seamless climate prediction has been increasing in a range of applications. Although sub-seasonal prediction is known for the low predictability, there has been significant progress from both the weather and climate communities. Following the launch of the various S2S prediction projects, there has been substantial effort and progress in understanding the S2S prediction. The source of sub-seasonal predictability come from various processes in the atmosphere, ocean, sea-ice, and land (such as MJO, soil moisture, snow cover, stratosphere condition, and ocean status), although they are not yet fully understood. Under certain conditions, these sources of predictability may improve the forecasting.

In this Special Issue, all contributions addressing methodological advancement for verification metrics for deterministic and probabilistic forecasts, and advances in bias removal on ensemble prediction are welcome. In addition, authors are encouraged to consider including a variety of topics related to the evaluation of the ensemble forecast quality of operational prediction models, as well as the development of methodologies for the calibration of the time range from sub-seasonal to seasonal prediction. Climate modeling aspects for more reliable sub-seasonal predictions such as initialization, ensemble prediction, resolution, and model physics processes, are also invited.

Dr. Suryun Ham
Dr. Hiroyuki Murakami
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sub-seasonal climate prediction 
  • seasonal climate prediction 
  • climate prediction model 
  • sub-seasonal prediction skill 
  • ensemble prediction 
  • climate modeling

Published Papers (3 papers)

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Research

17 pages, 52419 KiB  
Article
Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs
by Douglas M. Hultstrand, Steven R. Fassnacht, John D. Stednick and Christopher A. Hiemstra
Atmosphere 2022, 13(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13010003 - 21 Dec 2021
Cited by 2 | Viewed by 2293
Abstract
A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, [...] Read more.
A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, USA, to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data. Full article
(This article belongs to the Special Issue Recent Progress and Future Prospects of Subseasonal and Seasonal Climate Prediction)
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14 pages, 39429 KiB  
Article
Analysis of a Short-Term and a Seasonal Precipitation Forecast over Kenya
by Sara Miller, Vikalp Mishra, W. Lee Ellenburg, Emily Adams, Jason Roberts, Ashutosh Limaye and Robert Griffin
Atmosphere 2021, 12(11), 1371; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12111371 - 20 Oct 2021
Cited by 6 | Viewed by 1894
Abstract
Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow [...] Read more.
Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction. Full article
(This article belongs to the Special Issue Recent Progress and Future Prospects of Subseasonal and Seasonal Climate Prediction)
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15 pages, 16340 KiB  
Article
Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia
by Suryun Ham and Yeomin Jeong
Atmosphere 2021, 12(10), 1311; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12101311 - 07 Oct 2021
Cited by 2 | Viewed by 1644
Abstract
In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based [...] Read more.
In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based on a fully coupled atmosphere, land, ocean, and sea ice model. To examine the fidelity of the system with respect to reproducing and forecasting phenomena, this study assesses the systematic biases in the global prediction model focusing on the prediction skill for the East Asian winter monsoon (EAWM), which is a major driver of weather and climate variability in East Asia. To investigate the error characteristics of GloSea5, the hindcast period is analyzed by dividing it into two periods: 1991–2000 and 2001–2010. The main results show that the prediction skill for the EAWM with a lead time of 3 weeks is significantly decreased in the 2000s compared to the 1990s. To investigate the reason for the reduced EAWM prediction performance in the 2000s, the characteristics of the teleconnections relating to the polar and equatorial regions are examined. It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased EAWM prediction skill. Full article
(This article belongs to the Special Issue Recent Progress and Future Prospects of Subseasonal and Seasonal Climate Prediction)
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