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1. Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?

   https://doi.org/10.1175/JHM-D-19-0284.1  

   Miller, Paul W. ; Ramseyer, Craig A. ( June 2020 , Journal of Hydrometeorology)

   null (Ed.)

   Abstract In groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years. 

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2. Evaluating the Alaska Blocking Index as an indicator of wildfire potential in Alaska's central eastern interior

   https://doi.org/10.1002/joc.8450  

   Ballinger, Thomas_J ; Lader, Rick_T ; Bieniek, Peter_A ; Strader, Heidi ; Ziel, Robert ; Bhatt, Uma_S ; Borries‐Strigle, Cecilia ; Hostler, Joshua ; Stevens, Eric ; Waigl, Christine_F ; et al ( March 2024 , International Journal of Climatology)

   Increased Arctic air temperatures and evaporative fluxes have coincided with more frequent and destructive high‐latitude wildfires. Arctic fires impact ecosystems and people, especially at the community‐level by degrading air quality, destroying agriculture, and threatening life and property. Central Eastern Interior (CEI) Alaska is one such region that has recently experienced the effects of wildfire activity related to warming air temperatures. To improve our ability to identify fire weather events and assess their potential for extreme outbreaks at actionable lead times relevant to fire weather forecasters and managers, new metrics and approaches need to be established and applied toward understanding the physical mechanisms underlying such wildland fire characteristics. Our study uses a new, regional atmospheric circulation metric, the Alaska Blocking Index (ABI), to describe midtropospheric air pressure around Alaska, which is subsequently related to CEI fire weather conditions at the Predictive Service Area (PSA) scale in climatological and extreme events frameworks. Of note, during years of high fire activity, Build‐Up Index (BUI) values tend to be anomalously high during the duff and drought phases across the CEI PSAs, though comparatively lower BUI values are still associated with high fire activity in the Tanana Zone‐South (AK03S) PSA. Likewise, extreme BUI values are strongly tied to high ABI values and well‐defined upper‐air ridging circulation patterns in the duff and drought periods. The statistical skill of mean daily ABI values in the 6–10 day period preceding extreme duff period BUI values is modest (τ² > 14%) in the Upper Yukon Valley (AK02) PSA, a hotbed of wildland fire activity. Extremes in ABI and CEI BUI often occur in tandem, yielding regional predictability of upper‐air weather patterns and extremes and underlying surface weather conditions, by statistical and/or dynamical forecast models, imperative for local community and governmental organizations to effectively manage and allocate Alaska's fire weather resources.

    

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3. Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

   https://doi.org/10.1111/1752-1688.12746  

   Baker, Sarah A. ; Wood, Andrew W. ; Rajagopalan, Balaji ( April 2019 , JAWRA Journal of the American Water Resources Association)

   We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.

    

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4. Downscaled subseasonal fire danger forecast skill across the contiguous United States

   https://doi.org/10.1002/asl.1165  

   Abatzoglou, John T. ; McEvoy, Daniel J. ; Nauslar, Nicholas J. ; Hegewisch, Katherine C. ; Huntington, Justin L. ( March 2023 , Atmospheric Science Letters)

   The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

    

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5. Skillful Subseasonal Prediction of United States Extreme Warm Days and Standardized Precipitation Index in Boreal Summer

   https://doi.org/10.1175/JCLI-D-20-0878.1  

   Miller, Douglas E. ; Wang, Zhuo ; Li, Bo ; Harnos, Daniel S. ; Ford, Trent ( May 2021 , Journal of Climate)

   null (Ed.)

   Abstract Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States (US). We use a leading principal component of US soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP’s Climate Forecast System version 2 (CFSv2) at weeks 3-4 in the eastern US. It is found that the North Pacific SST anomalies persist several weeks and are associated with a persistent wave train pattern (WTZ500), which leads to increased occurrences of blocking and extreme temperature over the eastern US. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and decrease in latent heat flux, which may help maintain the overlying anticyclone. The clear sky conditions associated with blocking anticyclones further decrease soil moisture conditions and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, reservoir operation, and provide mitigation of impacts from extreme heat events. 

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