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Duo Chan and Peter Huybers

Abstract

Most historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation.

Open access
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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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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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
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.

Open access
Matthew D. Flournoy, Michael C. Coniglio, and Erik N. Rasmussen

Abstract

Although environmental controls on bulk supercell potential and hazards have been studied extensively, relationships between environmental conditions and temporal changes to storm morphology remain less explored. These relationships are examined in this study using a compilation of sounding data collected during field campaigns from 1994 to 2019 in the vicinity of 216 supercells. Environmental parameters are calculated from the soundings and related to storm-track characteristics like initial cell motion and the time of the right turn (i.e., the time elapsed between the cell initiation and the first time when the supercell obtains a quasi-steady motion that is directed clockwise from its initial motion.). We do not find any significant associations between environmental parameters and the time of the right turn. Somewhat surprisingly, no relationship is found between storm-relative environmental helicity and the time elapsed between cell initiation and the onset of deviant motion. Initial cell motion is best approximated by the direction of the 0–6-km mean wind at two-thirds the speed. This is a result of advection and propagation in the 0–4- and 0–2-km layers, respectively. Unsurprisingly, Bunkers-right storm motion is a good estimate of post-turn motion, but storms that exhibit a post-turn motion left of Bunkers-right are less likely to be tornadic. These findings are relevant for real-time forecasting efforts in predicting the path and tornado potential of supercells up to hours in advance.

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Yang Liu, Laurens Bogaardt, Jisk Attema, and Wilco Hazeleger

Abstract

Operational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future.

Open access