Does An ENSO-Conditional Skill Mask Improve Seasonal Predictions?

Abstract The National Centers for Environmental Prediction Climate Prediction Center uses statistical tools together with the Climate Forecast System (CFS) to produce forecasts for seasonal outlooks of U.S. temperature and precipitation. They are combined using an optimal weighting procedure that depends on a skill mask consisting of the average historical forecast skill of each tool. However, it is likely that skill during El Niño–Southern Oscillation events is higher and the use of this information in developing forecasts could lead to improved seasonal predictions. This study explores the potential to improve the skill of seasonal predictions by developing an ENSO-conditional skill mask. The conditional masks are developed in a perfect-model framework using the CFS version 2 hindcasts and two indices of ENSO. The skill of the indices in forecasting variations in conditional skill is evaluated. The ENSO-conditional skill masks provide improvements in correlation skill over the unconditional mask when averaged over the globe. The masks are applied to tercile forecasts of seasonal temperature and precipitation during the spring and forecasts are verified in a perfect-model context. Application of the conditional masks to tercile forecasts results in modified Heidke skill scores of more than 10% less than using the average mask for temperature and little difference in skill for precipitation. This is attributed to the larger number of equal chances forecasts when using the conditional masks, particularly for temperature. For precipitation, the skill predicted by the average and conditional masks is frequently below 0.3, leading to low skill regardless of which mask is used.

Download Full-text

Information Content of Seasonal Forecasts in a Changing Climate

Advances in Meteorology ◽

10.1155/2013/480210 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 11

Author(s):

Nir Y. Krakauer ◽

Michael D. Grossberg ◽

Irina Gladkova ◽

Hannah Aizenman

Keyword(s):

Information Gain ◽

Seasonal Forecasts ◽

Temperature And Precipitation ◽

Skill Scores ◽

The Usa ◽

Precipitation Anomalies ◽

The Mean ◽

Climate Prediction Center

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

Download Full-text

Projecting “Normals” in a Nonstationary Climate

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0267.1 ◽

2013 ◽

Vol 52 (2) ◽

pp. 289-302 ◽

Cited By ~ 14

Author(s):

D. S. Wilks

Keyword(s):

Climate Changes ◽

Real Data ◽

Seasonal Temperature ◽

Recent Past ◽

Statistical Estimates ◽

Temperature And Precipitation ◽

Nonstationary Climate ◽

Near Future ◽

Climate Prediction Center ◽

The U.S

AbstractClimate “normals” are statistical estimates of present and/or near-future climate means for such quantities as seasonal temperature or precipitation. In a changing climate, simply averaging a large number of previous years of data may not be the best method for estimating normals. Here eight formulations for climate normals, including the recently proposed “hinge” function, are compared in artificial- and real-data settings. Although the hinge function is attractive conceptually for representing accelerating climate changes simply, its use is in general not yet justified for divisional U.S. seasonal temperature or precipitation. Averages of the most recent 15 and 30 yr have performed better during the recent past for U.S. divisional seasonal temperature and precipitation, respectively; these averaging windows are longer than those currently employed for this purpose at the U.S. Climate Prediction Center.

Download Full-text

Intercomparison of Daily Precipitation Statistics over the United States in Observations and in NCEP Reanalysis Products

Journal of Climate ◽

10.1175/2010jcli3638.1 ◽

2010 ◽

Vol 23 (17) ◽

pp. 4637-4650 ◽

Cited By ~ 28

Author(s):

R. W. Higgins ◽

V. E. Kousky ◽

V. B. S. Silva ◽

E. Becker ◽

P. Xie

Keyword(s):

United States ◽

Daily Precipitation ◽

Southern Oscillation ◽

The United States ◽

Department Of Energy ◽

Climate Forecast System ◽

Daily Data ◽

Environmental Prediction ◽

New Generation ◽

Climate Prediction Center

Abstract A comparison of the statistics of daily precipitation over the conterminous United States is carried out using gridded station data and three generations of reanalysis products in use at the National Centers for Environmental Prediction (NCEP). The reanalysis products are the NCEP–NCAR reanalysis (Kalnay et al.), the NCEP–Department of Energy (DOE) reanalysis (Kanamitsu et al.), and the NCEP Climate Forecast System (CFS) reanalysis (Saha et al.). Several simple measures are used to characterize relationships between the observations and the reanalysis products, including bias, precipitation probability, variance, and correlation. Seasonality is accounted for by examining these measures for four nonoverlapping seasons, using daily data in each case. Relationships between daily precipitation and El Niño–Southern Oscillation (ENSO) phase are also considered. It is shown that the CFS reanalysis represents a clear improvement over the earlier reanalysis products, though significant biases remain. Comparisons of the error patterns in the reanalysis products provide a suitable basis for confident conversion of the Climate Prediction Center (CPC) operational monitoring and prediction products to the new generation of analyses based on CFS.

Download Full-text

Variation characteristics of temperature and precipitation on the northern slopes of the Himalaya region from 1979 to 2018

10.5194/egusphere-egu21-16556 ◽

2021 ◽

Author(s):

Yizhe Han ◽

Yaoming Ma ◽

Zhongyan Wang ◽

Weiqiang Ma

Keyword(s):

Climate Change ◽

Atmospheric Circulation ◽

Temperature Increase ◽

Southern Oscillation ◽

Monsoon Season ◽

Western Pacific Subtropical High ◽

Warming Trend ◽

Seasonal Temperature ◽

Temperature And Precipitation ◽

The World

The northern slopes of Himalaya (NSH) have the highest average elevation in the world. It is difficult to assess how climate change has affected this region because only a few observations are available from the high terrain and harsh environment. This study investigates the long-term characteristics of temperature and precipitation in the NSH. Further, the association of these variations with atmospheric circulation patterns is also investigated. Our results indicated that the warming trend in this region is almost 1.5 times that of the TP region, 2 times that of China, and 3.5 times that of the world. Additionally, the warming rate of the NSH is more obvious than other regions in the Himalayas, which shows that different regions of the Himalayas have different sensitivity to climate change. Although the warming trend in the NSH region does not show obvious seasonal differences like the TP, the temperature increase rate in autumn and winter is still higher than that in spring and summer. The abrupt change point for the temperature increase in summer was about 5 years later than that in other seasons, indicating that the NSH region is more sensitive to climate warming in cooler seasons, which is similar to the western and northwestern Himalaya. Furthermore, the Southern Oscillation Index (SOI) displays significant relationships with the temperature in the NSH, meanwhile, the North Atlantic Oscillation index (NAO) and Western Pacific Subtropical High Intensity Index (WPI) also exist some correlations with seasonal temperature change. This indicating that the atmospheric circulation would also have affected the temperature increase in this region, especially in summer and winter. The changes in precipitation are only affected by the SOI during the monsoon season (June to September), indicating that ENSO influences precipitation changes through water vapor transmission. In contrast, the precipitation in the TP is correlated with NAO, SOI and WPI, which indicating the precipitation of the TP might be affected by multiple circulation systems.&#160;&#160;

Download Full-text

Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States

Weather and Forecasting ◽

10.1175/waf-d-16-0117.1 ◽

2017 ◽

Vol 32 (1) ◽

pp. 327-341 ◽

Cited By ~ 5

Author(s):

Renaud Barbero ◽

John T. Abatzoglou ◽

Katherine C. Hegewisch

Keyword(s):

United States ◽

Statistical Downscaling ◽

North American ◽

Southern Oscillation ◽

Western United States ◽

Seasonal Temperature ◽

Multimodel Ensemble ◽

Temperature And Precipitation ◽

Spatial Disaggregation ◽

North American Multimodel Ensemble

AbstractThe skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Download Full-text

Developments in Operational Long-Range Climate Prediction at CPC

Weather and Forecasting ◽

10.1175/2007waf2007042.1 ◽

2008 ◽

Vol 23 (3) ◽

pp. 496-515 ◽

Cited By ~ 54

Author(s):

Edward A. O’Lenic ◽

David A. Unger ◽

Michael S. Halpert ◽

Kenneth S. Pelman

Keyword(s):

Long Range ◽

Southern Oscillation ◽

Skill Score ◽

Climate Prediction ◽

Direct Access ◽

Production Methods ◽

Heidke Skill Score ◽

User Access ◽

Environmental Prediction ◽

Climate Prediction Center

Abstract The science, production methods, and format of long-range forecasts (LRFs) at the Climate Prediction Center (CPC), a part of the National Weather Service’s (NWS’s) National Centers for Environmental Prediction (NCEP), have evolved greatly since the inception of 1-month mean forecasts in 1946 and 3-month mean forecasts in 1982. Early forecasts used a subjective blending of persistence and linear regression-based forecast tools, and a categorical map format. The current forecast system uses an increasingly objective technique to combine a variety of statistical and dynamical models, which incorporate the impacts of El Niño–Southern Oscillation (ENSO) and other sources of interannual variability, and trend. CPC’s operational LRFs are produced each midmonth with a “lead” (i.e., amount of time between the release of a forecast and the start of the valid period) of ½ month for the 1-month outlook, and with leads ranging from ½ month through 12½ months for the 3-month outlook. The 1-month outlook is also updated at the end of each month with a lead of zero. Graphical renderings of the forecasts made available to users range from a simple display of the probability of the most likely tercile to a detailed portrayal of the entire probability distribution. Efforts are under way at CPC to objectively weight, bias correct, and combine the information from many different LRF prediction tools into a single tool, called the consolidation (CON). CON ½-month lead 3-month temperature (precipitation) hindcasts over 1995–2005 were 18% (195%) better, as measured by the Heidke skill score for nonequal chances forecasts, than real-time official (OFF) forecasts during that period. CON was implemented into LRF operations in 2006, and promises to transfer these improvements to the official LRF. Improvements in the science and production methods of LRFs are increasingly being driven by users, who are finding an increasing number of applications, and demanding improved access to forecast information. From the forecast-producer side, hope for improvement in this area lies in greater dialogue with users, and development of products emphasizing user access, input, and feedback, including direct access to 5 km × 5 km gridded outlook data through NWS’s new National Digital Forecast Database (NDFD).

Download Full-text

Correlation Analysis of Seasonal Temperature and Precipitation in a Region of Southern Italy

Geosciences ◽

10.3390/geosciences8050160 ◽

2018 ◽

Vol 8 (5) ◽

pp. 160 ◽

Cited By ~ 1

Author(s):

Ennio Ferrari ◽

Roberto Coscarelli ◽

Beniamino Sirangelo

Keyword(s):

Correlation Analysis ◽

Southern Italy ◽

Seasonal Temperature ◽

Temperature And Precipitation

Download Full-text

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-381.1 ◽

2004 ◽

Vol 5 (6) ◽

pp. 1076-1090 ◽

Cited By ~ 52

Author(s):

Kevin Werner ◽

David Brandon ◽

Martyn Clark ◽

Subhrendu Gangopadhyay

Keyword(s):

Nearest Neighbor ◽

Southern Oscillation ◽

Climate Index ◽

Climate Information ◽

Streamflow Prediction ◽

Forecast System ◽

Ensemble Forecasts ◽

Weighting Schemes ◽

Temperature And Precipitation ◽

Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

Download Full-text

Benchmarking homogenization algorithms for monthly data

Climate of the Past ◽

10.5194/cp-8-89-2012 ◽

2012 ◽

Vol 8 (1) ◽

pp. 89-115 ◽

Cited By ~ 202

Author(s):

V. K. C. Venema ◽

O. Mestre ◽

E. Aguilar ◽

I. Auer ◽

J. A. Guijarro ◽

...

Keyword(s):

Time Series ◽

Performance Metrics ◽

Linear Trend ◽

Simulated Data ◽

Integrated Approach ◽

Global Network ◽

Temperature And Precipitation ◽

Skill Scores ◽

Simulated Network ◽

The Individual

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Download Full-text

Differential Credibility of Climate Modes in CMIP6

Journal of Climate ◽

10.1175/jcli-d-21-0359.1 ◽

2021 ◽

pp. 1

Author(s):

Jacob Coburn ◽

S.C. Pryor

Keyword(s):

Southern Oscillation ◽

Probability Distributions ◽

Critical Role ◽

Regional Scale ◽

Southern Annular Mode ◽

Climate Projections ◽

Annular Mode ◽

Climate Modes ◽

First Order ◽

Skill Scores

AbstractThis work quantitatively evaluates the fidelity with which the Northern Annular Mode (NAM), Southern Annular Mode (SAM), Pacific-North American pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) and the first-order mode interactions are represented in Earth System Model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM-PNA first-order interaction and underestimate the NAM-AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near-term.

Download Full-text