Optimal Ranking Regime Analysis of U.S. Summer Temperature and Degree-Days: 1895–2015

The optimal ranking regime (ORR) method was applied to mean summer maximum (TMXS) and mean summer minimum (TMNS) temperature and to cumulative summer cooling degree-days (CDDS) calculated from U.S. climate-division data during 1895–2015. CDDS is proposed as a proxy for growing degree-days for summer corn given their high rank correlation in station data during 1950–2014. The TMXS and CDDS ORR analyses show similar climate-regime patterns. Western and northeastern divisions experienced multidecadal cool periods before 1930 and warm periods after 1990. The 1930s drought appears as decadal warm regimes over the Midwest and Great Plains. Multidecadal TMXS and CDDS temperature cycles are evident over the Southeast, but TMXS and CDDS variation over the Midwest’s Corn Belt agricultural region has been regime free since the early 1940s. By contrast, TMNS regimes consistent with centennial-scale warming trends are found over most divisions outside the Southeast. From the multidecadal regime patterns detected by the ORR analyses, the TMXS, TMNS, and CDDS series of each climate division were tested for significant linear trends during 1910–2015 and 1970–2015. Significant positive TMNS trends during 1910–2015 are found in 48 of the 102 divisions, with some western trend magnitudes being greater than 15% of the twentieth-century climatological mean. During 1970–2015, positive TMXS trends are detected over 39 western and northeastern divisions, but warming TMNS trends are evident nationally. In some cooler western divisions, positive 1970–2015 CDDS trend magnitudes exceed 90% of the climatological mean. Consistent with the ORR analyses, Corn Belt TMXS and CDDS trends are insignificant during 1970–2015.

Assessment of Regional Drought Trend and Risk over China: A Drought Climate Division Perspective

A combination of Ward’s and k-means clustering was applied to a 3-month standardized precipitation index (SPI-03), and eight divisions of homogeneous drought variation throughout China were identified from the perspective of meteorological and agricultural droughts. A greater meridional gradient appeared over eastern China (six divisions) than over western China (two divisions). The climate division facilitated the evaluating of not only regional but also widespread droughts. Trend evaluation showed that western north China (WNC) has become increasingly wet in recent decades, while northern northeast China (NNE) has become increasingly dry. The Yangtze River valley (YZ) tended to experience less and weaker drought after the late 1970s. Southern northeast China (SNE) and the southwestern China–Tibetan Plateau (SW-TP) showed a decreasing trend in long-term but not short-term SPIs, implying that long-term drought conditions might develop continuously, thus allowing the following droughts to develop more rapidly and with a stronger intensity. Examination of the drought risk under El Niño revealed that northern regions were likely to suffer from drought rather than flood in the developing phase and the reverse in the decaying phase. Southeastern China (SE) and the YZ were vulnerable to flood rather than drought in the mature and decaying spring, with SE subjected to drought in the decaying summer. Such a distinctive regional pattern of drought risks was closely connected with the abnormal moisture supply patterns modulated by ENSO in different phases.

Developing and Evaluating Drought Indicators for Decision-Making

Drought indicators can help to detect, assess, and reduce impacts of drought. However, existing indicators often have deficiencies that limit their effectiveness, such as statistical inconsistency, noncomparability, arbitrary metrics, and lack of historic context. Further, indicators selected for drought plans may be only marginally useful, and relatively little prior work has investigated ways to design operationally practical indicators. This study devises a generalizable approach, based on feedback from users, to develop and evaluate indicators for decision-making. This approach employs a percentile-based framework that offers clarity, consistency, and comparability among different indicators, drought levels, time periods, and spatial scales. In addition, it characterizes the evolution of droughts and quantifies their severity, duration, and frequency. User preferences are incorporated into the framework’s parameters, which include percentile thresholds for drought onset and recovery, severity levels, anomalies, and consecutive time periods for triggering. To illustrate the approach and decision-making implications, the framework is applied to California Climate Division 2 and is used with decision-makers, water managers, and other participants in the National Integrated Drought Information System (NIDIS) California Pilot. Stakeholders report that the framework provides an easily understood and beneficial way to assess and communicate drought conditions, validly compare multiple indicators across different locations and time scales, quantify risks relative to historic droughts, and determine indicators that would be valuable for decision-making.

Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenarios, climate projections, and ecosystem simulations using the ISI-MIP results

We examined the changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVMs) obtained from the Inter-Sectoral Impact Model Intercomparison Project. Simulation results were obtained using five global climate models (GCMs) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (i.e., emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. At the end of the simulation period, changes from the year 2000 in all three variables varied considerably from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC and SOC projections, GVMs are the main influence on uncertainties (60 % and 90 %, respectively) rather than climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in vegetation carbon residence times is dominated by GVM uncertainty, particularly in the latter half of the 21st century. In addition, we found that the contribution of each uncertainty source is spatiotemporally heterogeneous and it differs among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division becomes cooler (from ca. 80 % in the equatorial division to 40 % in the snow division). Our results suggest that to assess climate change impacts on global ecosystem C cycling among each RCP scenario, the long-term C dynamics within the ecosystems (i.e., vegetation turnover and soil decomposition) are more critical factors than photosynthetic processes. The different trends in the contribution of uncertainty sources in each variable among climate divisions indicate that improvement of GVMs based on climate division or biome type will be effective. On the other hand, in dry regions, GCMs are the dominant uncertainty source in climate impact assessments of vegetation and soil C dynamics.

Optimal Ranking Regime Analysis of Intra- to Multidecadal U.S. Climate Variability. Part I: Temperature*

The optimal ranking regime (ORR) method was used to identify intradecadal to multidecadal (IMD) time windows containing significant ranking sequences in U.S. climate division temperature data. The simplicity of the ORR procedure’s output—a time series’ most significant nonoverlapping periods of high or low rankings—makes it possible to graphically identify common temporal breakpoints and spatial patterns of IMD variability in the analyses of 102 climate division temperature series. This approach is also applied to annual Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) climate indices, a Northern Hemisphere annual temperature (NHT) series, and divisional annual and seasonal temperature data during 1896–2012. In addition, Pearson correlations are calculated between PDO, AMO, and NHT series and the divisional temperature series. Although PDO phase seems to be an important influence on spring temperatures in the northwestern United States, eastern temperature regimes in annual, winter, summer, and fall temperatures are more coincident with cool and warm phase AMO regimes. Annual AMO values also correlate significantly with summer temperatures along the Eastern Seaboard and fall temperatures in the U.S. Southwest. Given evidence of the abrupt onset of cold winter temperatures in the eastern United States during 1957/58, possible climate mechanisms associated with the cause and duration of the eastern U.S. warming hole period—identified here as a cool temperature regime occurring between the late 1950s and late 1980s—are discussed.

Las potencias medias en la arquitectura climática global: la hibridación de la brecha Norte-Sur.

The aim of this contribution is to analyze in what sense the BASIC modify global climate change architecture, focusing on the North-South climate division and its persistence as an analytical category. The hypothesis is that the BASIC group tends to hybridize the North-South climate division as a result of the discord generated by their positions in contrast to the G77.