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Zhaolu Hou, Jianping Li, and Bin Zuo

Abstract

Numerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the local dynamical analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA correction scheme. The LDA correction scheme was first applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System, version 2. The results demonstrated that the LDA correction scheme improves forecast skill in many regions as measured by the correlation coefficient and root-mean-square error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño–Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission, and the forecast skill of central Pacific ENSO also increases due to the LDA correction method. The intensity of the ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA correction scheme on the probability forecast of cold and warm events. Overall, the LDA correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

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Michael A. Alexander and James D. Scott
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Stephen Jewson, Sebastian Scher, and Gabriele Messori

Abstract

Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost–loss model. Within this extended cost–loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.

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Weixin Xu, Steven A. Rutledge, and Kyle Chudler

Abstract

Using 17-yr spaceborne precipitation radar measurements, this study investigates how diurnal cycles of rainfall and convective characteristics over the South China Sea region are modulated by the boreal summer intraseasonal oscillation (BSISO). Generally, diurnal cycles change significantly between suppressed and active BSISO periods. Over the Philippines and Indochina, where the low-level monsoon flows impinge on coast lines, diurnal cycles of rainfall and many convective properties are enhanced during suppressed periods. During active periods, diurnal variation of convection is still significant over land but diminishes over water. Also, afternoon peaks of rainfall and MCS populations over land are obviously extended in active periods, mainly through the enhancement of stratiform precipitation. Over Borneo, where the prevailing low-level winds are parallel to coasts, diurnal cycles (both onshore and offshore) are actually stronger during active periods. Radar profiles also demonstrate a pronounced nocturnal offshore propagation of deep convection over western Borneo in active periods. During suppressed periods, coastal afternoon convection over Borneo is reduced, and peak convection occurs over the mountains until the convective suppression is overcome in the late afternoon or evening. A major portion (>70%) of the total precipitation over the Philippines and Indochina during suppressed periods falls from afternoon isolated to medium-sized systems (<10 000 km2), but more than 70% of the active BSISO rainfall is contributed by nocturnal (after 1800 LT) broad precipitation systems (>10 000 km2). However, offshore total precipitation is dominated by large precipitation systems (>10 000 km2) regardless of BSISO phases and regions.

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Daniel Watters, Alessandro Battaglia, and Richard P. Allan

Abstract

NASA Precipitation Measurement Mission observations are used to evaluate the diurnal cycle of precipitation from three CMIP6 models (NCAR-CESM2, CNRM-CM6.1, CNRM-ESM2.1) and the ERA5 reanalysis. NASA’s global-gridded IMERG product, which combines spaceborne microwave radiometer, infrared sensor, and ground-based gauge measurements, provides high-spatiotemporal-resolution (0.1° and half-hourly) estimates that are suitable for evaluating the diurnal cycle in models, as determined against the ground-based radar network over the conterminous United States. IMERG estimates are coarsened to the spatial and hourly resolution of the state-of-the-art CMIP6 and ERA5 products, and their diurnal cycles are compared across multiple decades of June–August in the 60°N–60°S domain (IMERG and ERA5: 2000–19; NCAR and CNRM: 1979–2008). Low-precipitation regions (and weak-amplitude regions when analyzing the diurnal phase) are excluded from analyses so as to assess only robust diurnal signals. Observations identify greater diurnal amplitudes over land (26%–134% of the precipitation mean; 5th–95th percentile) than over ocean (14%–66%). ERA5, NCAR, and CNRM underestimate amplitudes over ocean, and ERA5 overestimates over land. IMERG observes a distinct diurnal cycle only in certain regions, with precipitation peaking broadly between 1400 and 2100 LST over land (2100–0600 LST over mountainous and varying-terrain regions) and 0000 and 1200 LST over ocean. The simulated diurnal cycle is unrealistically early when compared with observations, particularly over land (NCAR-CESM2 AMIP: −1 h; ERA5: −2 h; CNRM-CM6.1 AMIP: −4 h on average) with nocturnal maxima not well represented over mountainous regions. Furthermore, ERA5’s representation of the diurnal cycle is too simplified, with less interannual variability in the time of maximum relative to observations over many regions.

Open access
Peng Wang, Yishuai Jin, and Zhengyu Liu

Abstract

In this study, we investigate a diurnal predictability barrier (DPB) for weather predictions using an idealized model and observations. This DPB is referred to a maximum drop of predictability (e.g., autocorrelation) at a particular time of the day, regardless of the initial time. Previous studies demonstrated that a strong seasonal cycle of El Niño–Southern Oscillation (ENSO) growth rate is responsible for the seasonal predictability barrier of the ENSO in spring. This led us to investigate whether or not a strong diurnal cycle may generate a DPB. We study the DPB using an idealized model, the Lorenz 1963 model, with the addition of a diurnal cycle. We find that diurnal growth rate can generate a DPB in this chaotic system, regardless of the initial error. Finally, by calculating the autocorrelation function using the hourly data of surface temperature, we explore the DPB at two stations in Wisconsin and Beijing, China. A clear DPB feature is found at both stations. The dramatic drop of predictability at a specific time of the day is likely due to the diurnal variation of the system. This is a new feature that needs further study for short-term weather predictions.

Open access
Michael J. Reeder, Thomas Spengler, and Clemens Spensberger

Abstract

It is thought that the sensible heat fluxes associated with sea surface temperature (SST) fronts can affect the genesis and evolution of atmospheric fronts. An analytic model is developed and used to explore this idea. The model predictions are compared with climatologies of atmospheric fronts over the North Atlantic Ocean identified in reanalyses. The climatologies are divided into times when fronts are detected at a point and times when they are not, and compared with model results with and without fronts in their initial conditions. In airstreams with fronts, both the climatologies and model show that adiabatic frontogenesis is much more important than diabatic frontogenesis. They also show that there is weak diabatic frontogenesis associated with differential sensible heating over the SST front and frontolysis either side of it. Because of the upstream and downstream frontolysis, the SST front has relatively little net effect on atmospheric fronts in the model. This result holds true as the width and strength of the SST front changes. In airstreams initially without fronts, a combination of adiabatic and diabatic frontogenesis is important for the local genesis of atmospheric fronts over the SST front. The model shows sustained frontogenesis only when the deformation is sufficiently strong or when the translation speed is low, as advection otherwise weakens the potential temperature gradient. This strong localized diabatic frontogenesis, which is amplified by adiabatic frontogenesis, can result in a front, which is consistent with atmospheric fronts in the region being most frequently located along the SST front.

Open access
Samar Minallah and Allison L. Steiner

Abstract

Lakes are an integral part of the geosphere, but they are challenging to represent in Earth system models, which either exclude lakes or prescribe properties without simulating lake dynamics. In the ECMWF interim reanalysis (ERA-Interim), lakes are represented by prescribing lake surface water temperatures (LSWT) from external data sources, while the newer-generation ERA5 introduces the Freshwater Lake (FLake) parameterization scheme to the modeling system with different LSWT assimilation data sources. This study assesses the performance of these two reanalyses over three regions with the largest lakes in the world (Laurentian Great Lakes, African Great Lakes, and Lake Baikal) to understand the effects of their simulation differences on hydrometeorological variables. We find that differences in lake representation alter the associated hydrological and atmospheric processes and can affect regional hydroclimatic assessments. There are prominent differences in LSWT between the two datasets that influence the simulation of lake-effect snowstorms in the Laurentian winters and lake–land-breeze circulation patterns in the African region. Generally, ERA5 has warmer LSWT in all three regions for most months (by 2–12 K) and its evaporation rates are up to twice the magnitudes in ERA-Interim. In the Laurentian lakes, ERA5 has strong biases in LSWT and evaporation magnitudes. Over Lake Baikal and the African Great Lakes, ERA5 LSWT magnitudes are closer to satellite-based datasets, albeit with a warm bias (1–4 K), while ERA-Interim underestimates the magnitudes. ERA5 also simulates intense precipitation hot spots in lake proximity that are not visible in ERA-Interim and other observation-based datasets. Despite these limitations, ERA5 improves the simulation of lake–land circulation patterns across the African Great Lakes.

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Feng Hu and Tim Li

Abstract

The effect of vertically tilted structure (VTS) of the MJO on its phase propagation speed was investigated through the diagnosis of ERA-Interim reanalysis data during 1979–2012. A total of 84 eastward propagating MJO events were selected. It was found that all MJO events averaged throughout their life cycles exhibited a clear VTS, and the tilting strength was significantly positively correlated to the phase speed. The physical mechanism through which the VTS influenced the phase speed was investigated. On the one hand, a stronger VTS led to a stronger vertical overturning circulation and a stronger descent in the front, which caused a greater positive moist static energy (MSE) tendency in situ through enhanced vertical MSE advection. The stronger MSE tendency gradient led to a faster eastward phase speed. On the other hand, the enhanced overturning circulation in front of MJO convection led to a stronger easterly/low pressure anomaly at the top of the boundary layer, which induced a stronger boundary layer convergence and stronger ascent in the lower troposphere. This strengthened the boundary layer moisture asymmetry and favored a faster eastward propagation speed.

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Toshichika Iizumi, Yonghee Shin, Jaewon Choi, Marijn van der Velde, Luigi Nisini, Wonsik Kim, and Kwang-Hyung Kim

Abstract

Forecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The National Agriculture and Food Research Organization–Asia-Pacific Economic Cooperation Climate Center (NARO-APCC) Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and 1 year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the U.S. Department of Agriculture (USDA). Forecasts for maize, wheat, soybean, and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the United States, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skillful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

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