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DOE OSTI2020

Decadal predictability of North Atlantic blocking and the NAO

Can multi-annual variations in the frequency of North Atlantic atmospheric blocking and mid-latitude circulation regimes be skilfully predicted? Recent advances in seasonal forecasting have shown that mid-latitude climate variability does exhibit significant predictability. However, atmospheric predictability has generally been found to be quite limited on multi-annual timescales. New decadal prediction experiments from NCAR are found to exhibit remarkable skill in reproducing the observed multi-annual variations of wintertime blocking frequency over the North Atlantic and of the North Atlantic Oscillation (NAO) itself. This is partly due to the large ensemble size that allows the predictable component of the atmospheric variability to emerge from the background chaotic component. The predictable atmospheric anomalies represent a forced response to oceanic low-frequency variability that strongly resembles the Atlantic Multi-decadal Variability (AMV), correctly reproduced in the decadal hindcasts thanks to realistic ocean initialization and ocean dynamics. The occurrence of blocking in certain areas of the Euro-Atlantic domain determines the concurrent circulation regime and the phase of known teleconnections, such as the NAO, consequently affecting the stormtrack and the frequency and intensity of extreme weather events. Therefore, skilfully predicting the decadal fluctuations of blocking frequency and the NAO may be used in statistical predictions of near-term climate anomalies, and it provides a strong indication that impactful climate anomalies may also be predictable with improved dynamical models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Abstract Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

Meteorology & Atmospheric Sciences↗

DOE OSTI2021

The use of soil moisture and Standardized Evaporative Stress Ratio (SESR) anomalies for increased lead time of the development flash drought and heat waves

Drought and rapid intensification of drought (i.e., flash drought) can have significant impacts on the natural environment and the economy, particularly when coupled with a period(s) of extreme heat (Otkin et al., 2018). The development and/or intensification of drought falls squarely in the sub-seasonal to seasonal (S2S) time frame, which dictates that drought development, intensification, and propagation could be an output of a subseasonal or seasonal forecast. Current S2S forecasts have poor skill for forecasting the development of extreme drought and heat waves.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

LANL Institutional Computing Report: Towards a digital twin of Arctic sea ice

Our goal is to develop a digital twin of Arctic sea ice that combines high-resolution predictive modeling with observational products. This effort will provide an optimized model for use in seasonal to sub-seasonal forecasting and a tool for policymakers to better anticipate, plan for, and mitigate the national security impacts of rapidly changing Arctic conditions. The modeling component uses the Discrete Element Model for Sea Ice (DEMSI), which uses discrete elements to represent the sea ice with an explicit representation of forces between them.

58 GEOSCIENCES↗

DOE OSTI2023

Are long-term climate projections useful for on-farm adaptation decisions?

The current literature on climate services for farmers predominantly focuses on seasonal forecasts, with an assumption that longer-term climate projections may not be suitable for informing farming decisions. In this paper, we explore whether certain types of long-term climate projections may be useful for some specific types of farming decisions. Through interviews with almond tree crop farmers and farm advisors in California, we examine how farmers perceive the utility and accuracy levels of long-term climate projections and identify the types of projections that they may find useful. The interviews revealed that farmers often perceive long-term climate projections as an extension of weather forecasts, which can lead to their initial skepticism of the utility of such information. However, we also found that when farmers were presented with long-term trends or shifts in crop-specific agroclimatic metrics (such as chill hours or summer heat), they immediately perceived these as valuable for their decision-making. Hence, the manner in which long-term projections are framed, presented, and discussed with farmers can heavily influence their perception of the potential utility of such projections. The iterative conversations as part of the exploratory interview questions, served as a tool for “ joint construction of meaning” of complex and ambiguous terms such as “long-term climate projections,” “long-term decisions” and “uncertainty.” This in-turn supported a joint identification (and understanding) of the types of information that can potentially be useful for on-farm adaptive decisions, where the farmer and the interviewer both improvise and iterate to find the best types of projections that fit specific decision-contexts. Overall, this research identifies both the types of long-term climate information that farmers may consider useful, and the engagement processes that are able to effectively elicit farmers' long-term information needs.

Jagannathan, Kripa↗

NASA NTRS1981

Spatial and Temporal scales of time-averaged 700 MB height anomalies

The monthly and seasonal forecasting technique is based to a large extent on the extrapolation of trends in the positions of the centers of time averaged geopotential height anomalies. The complete forecasted height pattern is subsequently drawn around the forecasted anomaly centers. The efficacy of this technique was tested and time series of observed monthly mean and 5 day mean 700 mb geopotential heights were examined. Autocorrelation statistics are generated to document the tendency for persistence of anomalies. These statistics are compared to a red noise hypothesis to check for evidence of possible preferred time scales of persistence. Space-time spectral analyses at middle latitudes are checked for evidence of periodicities which could be associated with predictable month-to-month trends. A local measure of the average spatial scale of anomalies is devised for guidance in the completion of the anomaly pattern around the forecasted centers.

Gutzler, D.↗

NASA NTRS1999

The Variability and Predictability of Precipitation over Southeast Asian

The large scale atmospheric moisture budget, including precipitation, moisture flux convergence, and evaporation is described over southeast Asian. ne results are from the Goddard Earth Observing System (GEOS-1) data assimilation system for the period March 1981 - February 1995. On average, in the late spring and early summer season, precipitation is largely balanced by the atmospheric moisture flux convergence over southeast Asian; evaporation is also an important contributor. The seasonal variability of precipitation is best related to variations in the moisture flux convergence. On the monthly time scales, both the precipitable water and precipitation anomalies are most closely related to moisture flux convergence anomalies and less closely related to evaporation anomalies. The impact of the low-level jet (LLJ) on the strong low-level moisture flux convergence will be discussed. An ensemble of GEOS-2 atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulation are analyzed to assess the controlling influences of boundary forcing and memory of initial conditions. We also assess the variability and potential predictability from these experiments.

Chang, Yehui↗

NASA NTRS2005

CDEP Consortium on Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI)

The ODASI consortium is focused activity of the NOAA/OGP/Climate Diagnostics and Experimental Prediction Program with the goal of improving ocean data assimilation methods and their implementations in support of seasonal forecasts with coupled general circulation models. The consortium is undertaking coordinated assimilation experiments, with common forcing data sets and common input data streams. With different assimilation systems and different models, we aim to understand what approach works best in improving forecast skill in the equatorial Pacific. The presentation will provide an overview of the consortium goals and plans and recent results focused towards evaluating data impacts.

Rienecker, Michele↗

NASA NTRS2005

NASA Supercomputer Improves Prospects for Ocean Climate Research

Estimates of ocean circulation constrained by in situ and remotely sensed observations have become routinely available during the past five years, and they are being applied to myriad scientific and operational problems [Stammer et al.,2002]. Under the Global Ocean Data Assimilation Experiment (GODAE), several regional and global estimates have evolved for applications in climate research, seasonal forecasting, naval operations, marine safety, fisheries,the offshore oil industry, coastal management, and other areas. This article reports on recent progress by one effort, the consortium for Estimating the Circulation and Climate of the Ocean (ECCO), toward a next-generation synthesis of ocean and sea-ice data that is global, that covers the full ocean depth, and that permits eddies.

Estimating the Circulation and Climate of the Ocea↗

NASA NTRS2010

Soil Moisture Controls on Rainfall and Temperature Variability: A Modeler Searches Through Observational Data

The degree to which atmospheric processes respond to variations in soil moisture - a potentially important but largely untapped element of subseasonal to seasonal prediction - can be determined easily and directly for an atmospheric model but cannot be determined directly for nature through an analysis of observations. In atmospheric models) directions of causality can be artificially manipulated; we can avoid difficulties associated with the fact that atmospheric variations have a much larger impact on land state variations than vice-versa. In nature) on the other hand) the dominant direction of causality (the atmosphere forcing the ground) cannot be artificially "turned off") and the statistics associated with this dominant direction overwhelm those of the feedback signal. Observational data) however) do allow a number of indirect measures of landatmosphere feedback. This seminar reports on a series of joint analyses of observational and model data designed to illuminate the degree of land-atmosphere feedback present in the real world. The indirect measures do in fact suggest that feedback in nature, though small) is significant - enough to warrant the development of realistic land initialization strategies for subseasonal and seasonal forecasts.

Koster, Randal↗

NASA NTRS2009

The GLACE-2 Experiment

A major motivation for the study of the coupled land-atmosphere system is the idea that soil moisture anomalies may affect future meteorological variables through their effects on future surface energy and water budgets. If true, the accurate initialization of soil moisture in a subseasonal or seasonal forecast system may improve forecast skill, making the forecast products more valuable to society. The Global Land-Atmosphere Coupling Experiment (GLACE-2) project is examining, with a wide variety of models, the degree to which subseasonal (out to two months) precipitation and air temperature forecasts improve through the realistic initialization of soil moisture. For the first time ever, a global consensus should emerge regarding the value of land initialization for forecasts, perhaps motivating national forecast centers to make full use of land moisture initialization in their operations. Participants in GLACE-2 perform two series of forecasts, each consisting of 100 2-month forecast ensembles (10 members per ensemble) covering ten boreal spring and summer start-dates in each of the years 1986-1995. Series 1 utilizes realistic land surface state initialization, provided through a decadal offline simulation using realistic meteorological forcing, as provided by the Global Soil Wetness Project - Phase 2 (GSWP-2), a research activity of the Global Land-Atmosphere System Study (GLASS) of GEWEX. Series 2 is identical to Series 1 in every way except for the fact that it does not benefit from realistic land state initialization. Through the comparison of Series 1 and 2, we isolate the impact of land initialization on the forecasts. Optional extensions to these base runs include forecasts covering additional years, using alternative meteorological forcing for the land initialization. To date, GLACE-2 has garnered participation from eleven modeling groups, covering 13 atmospheric models. Analysis of available results is already well underway, both at the individual modeling institutions and at the NASA Goddard Space Flight Center, which is coordinating the project. Analysis focuses on two elements of the forecast problem: (i) the quantification of model-specific "predictability" (i.e., the degree to which simulated atmospheric chaos will foil a forecast, even under the assumption of"perfect" model physics, initialization data, and validation data) and its decay with time; and (ii) the quantification of forecast skill, determined through a comparison of predicted precipitation and air temperature against observations. Indeed, the specific contribution of land initialization to both these elements is isolated through a comparison of the Series 1 and 2 forecasts. We examine the two elements at four different forecast leads: 1-15, 16-30, 31-45, and 46-60 days. Statistics-based approaches for enhancing skill (essentially using observational statistics to reduce the impact of the models' climatic biases) are also tested. In the present talk, we provide an update of progress in GLACE-2, featuring quantifications of predictability and forecast skill for a number of the participating models and providing a "first look" at the desired consensus view of land impacts on subseasonal forecasts.

Koster, Randal↗

NASA NTRS2010

The Second Phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2)

Koster, Randal D.↗

NASA NTRS2012

On the Influence of North Pacific Sea Surface Temperature on the Arctic Winter Climate

Differences between two ensembles of Goddard Earth Observing System Chemistry-Climate Model simulations isolate the impact of North Pacific sea surface temperatures (SSTs) on the Arctic winter climate. One ensemble of extended winter season forecasts is forced by unusually high SSTs in the North Pacific, while in the second ensemble SSTs in the North Pacific are unusually low. High Low differences are consistent with a weakened Western Pacific atmospheric teleconnection pattern, and in particular, a weakening of the Aleutian low. This relative change in tropospheric circulation inhibits planetary wave propagation into the stratosphere, in turn reducing polar stratospheric temperature in mid- and late winter. The number of winters with sudden stratospheric warmings is approximately tripled in the Low ensemble as compared with the High ensemble. Enhanced North Pacific SSTs, and thus a more stable and persistent Arctic vortex, lead to a relative decrease in lower stratospheric ozone in late winter, affecting the April clear-sky UV index at Northern Hemisphere mid-latitudes.

Hurwitz, Margaret M.↗

NASA NTRS2014

Observation-Corrected Precipitation Estimates in GEOS-5

Several GEOS-5 applications, including the GEOS-5 seasonal forecasting system and the MERRA-Land data product, rely on global precipitation data that have been corrected with satellite and or gauge-based precipitation observations. This document describes the methodology used to generate the corrected precipitation estimates and their use in GEOS-5 applications. The corrected precipitation estimates are derived by disaggregating publicly available, observationally based, global precipitation products from daily or pentad totals to hourly accumulations using background precipitation estimates from the GEOS-5 atmospheric data assimilation system. Depending on the specific combination of the observational precipitation product and the GEOS-5 background estimates, the observational product may also be downscaled in space. The resulting corrected precipitation data product is at the finer temporal and spatial resolution of the GEOS-5 background and matches the observed precipitation at the coarser scale of the observational product, separately for each day (or pentad) and each grid cell.

Data Assimilation↗

NASA NTRS2017

Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Sea Ice↗

NASA NTRS2017

Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office

Arctic↗

NASA NTRS2017

The North Pacific Summer Jet and Climate Extremes over North America: Mechanisms and Model Biases

The North Pacific summer jet (NPSJ) plays a critical role as a waveguide for weather systems and other sub-seasonal Rossby waves entering North America and therefore has a controlling influence on the warm season weather and climate extremes over much of the continent. In particular, much of the warm season precipitation that occurs over the central United States depends on subseasonal transients that are able to tap moisture from the Gulf of Mexico as they propagate across the continent. The GEOS-5 atmospheric general circulation model (AGCM), like many AGCMs, is deficient in the simulation of the NPSJ. It is shown that the deficiency is composed of: 1) a stunted jet in which the strongest winds are confined to the Asian continent, failing to extend across the North Pacific into the Gulf of Alaska as observed, and 2) a zonally symmetric poleward shift in the jet. These biases combine to impede the eastward propagation of the weather systems into the continent (the stunted jet), and deprive those systems that do enter the continent access to the moisture from the Gulf (the northward shift), leading to a dry bias over the central US. It is shown that the stunted jet bias is the result of too strong heating that occurs just south of the jet core over and near Tibet. Furthermore, it is shown that the poleward shift of the NPSJ can be corrected in the current GEOS-5 AGCM by increasing the vertical resolution. The implications of these results for improving warm season forecasts of extreme events will be discussed.

NPS↗