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Aiguo Dai
,
Junhong Wang
,
Peter W. Thorne
,
David E. Parker
,
Leopold Haimberger
, and
Xiaolan L. Wang

Abstract

Radiosonde humidity records represent the only in situ observations of tropospheric water vapor content with multidecadal length and quasi-global coverage. However, their use has been hampered by ubiquitous and large discontinuities resulting from changes to instrumentation and observing practices. Here a new approach is developed to homogenize historical records of tropospheric (up to 100 hPa) dewpoint depression (DPD), the archived radiosonde humidity parameter. Two statistical tests are used to detect changepoints, which are most apparent in histograms and occurrence frequencies of the daily DPD: a variant of the Kolmogorov–Smirnov (K–S) test for changes in distributions and the penalized maximal F test (PMFred) for mean shifts in the occurrence frequency for different bins of DPD. These tests capture most of the apparent discontinuities in the daily DPD data, with an average of 8.6 changepoints (∼1 changepoint per 5 yr) in each of the analyzed radiosonde records, which begin as early as the 1950s and ended in March 2009. Before applying breakpoint adjustments, artificial sampling effects are first adjusted by estimating missing DPD reports for cold (T < −30°C) and dry (DPD artificially set to 30°C) conditions using empirical relationships at each station between the anomalies of air temperature and vapor pressure derived from recent observations when DPD reports are available under these conditions. Next, the sampling-adjusted DPD is detrended separately for each of the 4–10 quantile categories and then adjusted using a quantile-matching algorithm so that the earlier segments have histograms comparable to that of the latest segment. Neither the changepoint detection nor the adjustment uses a reference series given the stability of the DPD series.

Using this new approach, a homogenized global, twice-daily DPD dataset (available online at www.cgd.ucar.edu/cas/catalog/) is created for climate and other applications based on the Integrated Global Radiosonde Archive (IGRA) and two other data sources. The adjusted-daily DPD has much smaller and spatially more coherent trends during 1973–2008 than the raw data. It implies only small changes in relative humidity in the lower and middle troposphere. When combined with homogenized radiosonde temperature, other atmospheric humidity variables can be calculated, and these exhibit spatially more coherent trends than without the DPD homogenization. The DPD adjustment yields a different pattern of change in humidity parameters compared to the apparent trends from the raw data. The adjusted estimates show an increase in tropospheric water vapor globally.

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Jaxk Reeves
,
Jien Chen
,
Xiaolan L. Wang
,
Robert Lund
, and
Qi Qi Lu

Abstract

This review article enumerates, categorizes, and compares many of the methods that have been proposed to detect undocumented changepoints in climate data series. The methods examined include the standard normal homogeneity (SNH) test, Wilcoxon’s nonparametric test, two-phase regression (TPR) procedures, inhomogeneity tests, information criteria procedures, and various variants thereof. All of these methods have been proposed in the climate literature to detect undocumented changepoints, but heretofore there has been little formal comparison of the techniques on either real or simulated climate series. This study seeks to unify the topic, showing clearly the fundamental differences among the assumptions made by each procedure and providing guidelines for which procedures work best in different situations. It is shown that the common trend TPR and Sawa’s Bayes criteria procedures seem optimal for most climate time series, whereas the SNH procedure and its nonparametric variant are probably best when trend and periodic effects can be diminished by using homogeneous reference series. Two applications to annual mean temperature series are given. Directions for future research are discussed.

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Robert Lund
,
Xiaolan L. Wang
,
Qi Qi Lu
,
Jaxk Reeves
,
Colin Gallagher
, and
Yang Feng

Abstract

Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Classical changepoint tests based on sums of squared errors are modified to take into account series autocorrelations and periodicities. The methods are applied in the analyses of two climate series.

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Yalin Fan
,
Isaac M. Held
,
Shian-Jiann Lin
, and
Xiaolan L. Wang

Abstract

Surface wind (U 10) and significant wave height (Hs) response to global warming are investigated using a coupled atmosphere–wave model by perturbing the sea surface temperatures (SSTs) with anomalies generated by the Working Group on Coupled Modeling (WGCM) phase 3 of the Coupled Model Intercomparison Project (CMIP3) coupled models that use the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4)/Special Report on Emissions Scenarios A1B (SRES A1B) scenario late in the twenty-first century.

Several consistent changes were observed across all four realizations for the seasonal means: robust increase of U 10 and Hs in the Southern Ocean for both the austral summer and winter due to the poleward shift of the jet stream; a dipole pattern of the U 10 and Hs with increases in the northeast sector and decreases at the midlatitude during boreal winter in the North Atlantic due to the more frequent occurrence of the positive phases of the North Atlantic Oscillation (NAO); and strong decrease of U 10 and Hs in the tropical western Pacific Ocean during austral summer, which might be caused by the joint effect of the weakening of the Walker circulation and the large hurricane frequency decrease in the South Pacific.

Changes of the 99th percentile U 10 and Hs are twice as strong as changes in the seasonal means, and the maximum changes are mainly dominated by the changes in hurricanes. Robust strong decreases of U 10 and Hs in the South Pacific are obtained because of the large hurricane frequency decrease, while the results in the Northern Hemisphere basins differ among the models. An additional sensitivity experiment suggests that the qualitative response of U 10 and Hs is not affected by using SST anomalies only and maintaining the radiative forcing unchanged (using 1980 values), as in this study.

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Mark A. Hemer
,
Xiaolan L. Wang
,
Ralf Weisse
, and
Val R. Swail
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Xiaolan L. Wang
,
Mercè Casas-Prat
,
Yang Feng
,
Alexander Crosby
, and
Val R. Swail

Abstract

This study presents and analyzes Environment Canada’s Davis Strait Baffin Bay (EC-DSBB) Wind and Wave Reanalysis for the period 1979–2016 to characterize the historical changes in the surface wind speed and ocean surface waves. The trend analysis is carried out only for the months of May–December, when there is a significant ice-free sea area. The results show that 10-m wind speed (W s ) has increased significantly in most areas of the domain in September–December, with some significant decreases over the open water area in June and July. The W s increases are most extensive in September, with significant increases in both the mean and extremes. It is also shown that the mean wind direction (W d ) has a distinctive seasonal variation, being mainly northward and northwestward in June–August, and predominantly southward and southeastward in May and September–December. The most notable changes in W d are seen in June. The results also show that significant wave height (H s ) and wave power (W p ) have significantly increased in September–December and decreased in June. For example, the September regional mean H s has increased at a rate of 0.4% yr−1. In September–December, the local W s increases seem to be the main driver for the H s and W p increases, but the southeastward direction is favored by increasing fetch as sea ice retreats. In September and December, the positive trend in both W s and H s has intensified in the 2001–16. In June, however, the mean W d and the changes therein also play an important role in the H s changes, which are more affected by remotely generated waves.

Open access
Panfeng Zhang
,
Guoyu Ren
,
Yan Xu
,
Xiaolan L. Wang
,
Yun Qin
,
Xiubao Sun
, and
Yuyu Ren

Abstract

This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.

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Prashant Kumar
,
Seung-Ki Min
,
Evan Weller
,
Hansu Lee
, and
Xiaolan L. Wang

Abstract

Extreme ocean surface wave heights significantly affect coastal structures and offshore activities and impact many vulnerable populations of low-lying islands. Therefore, better understanding of ocean wave height variability plays an important role in potentially reducing risk in such regions. In this study, global impacts of natural climate variability such as El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific decadal oscillation (PDO) on extreme significant wave height (SWH) are analyzed using ERA-Interim (1980–2014) and ECMWF twentieth-century reanalysis (ERA-20C; 1952–2010) datasets for December–February (DJF). The nonstationary generalized extreme value (GEV) analysis is used to determine the influence of natural climate variability on DJF maxima of SWH (Hmax), wind speed (Wmax), and mean sea level pressure gradient amplitude (Gmax). The major ENSO influence on Hmax is found over the northeastern North Pacific (NP), with increases during El Niño and decreases during La Niña, and its counter responses are observed in coastal regions of the western NP, which are consistently observed in both Wmax and Gmax responses. The Hmax response to the PDO occurs over similar regions in the NP as those associated with ENSO but with much weaker amplitude. Composite analysis of different ENSO and PDO phase combinations reveals stronger (weaker) influences when both variability modes are of the same (opposite) phase. Furthermore, significant NAO influence on Hmax, Wmax, and Gmax is observed throughout Icelandic and Azores regions in relation to changes in atmospheric circulation patterns. Overall, the response of extreme SWH to natural climate variability modes is consistent with seasonal mean responses.

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Xiaolan L. Wang
,
Yang Feng
,
Vincent Y. S. Cheng
, and
Hong Xu

Abstract

This study first developed a comprehensive semiautomatic data homogenization procedure to produce gap-infilled and homogenized monthly precipitation data series for 425 long-term/critical stations in Canada, which were then used to assess Canadian historical precipitation trends. Data gaps in the 425 series were infilled by advanced spatial interpolation of a much larger dataset. The homogenization procedure repeatedly used multiple homogeneity tests without and with reference series to identify changepoints/inhomogeneities, the results from which were finalized by manual analysis using metadata and visual inspection of the multiphase regression fits. As a result, 298 out of the 425 data series were found to be inhomogeneous. These series were homogenized using quantile matching adjustments. The homogenized dataset shows better spatial consistency of trends than does the raw dataset. The improved gridding and regional mean trend estimation methods also provide more realistic trend estimates. With these improvements, Canadian historical precipitation trends were found to be dominantly positive and significant, except in central-south Canada where the trends are generally insignificant and small with mixed directions. For annual precipitation, the largest increases are seen in southeastern Canada and along the Pacific coast; however, the largest relative increases (in percent of the 1961–90 mean) are seen in northern Canada. The largest trend difference between northern and southern Canada is seen in winter, in which significant increases in the north were matched with significant decreases in the south.

Significance Statement

This study aims to produce a homogenized long-term monthly precipitation dataset for Canada, which is then used to assess Canadian historical precipitation trends. The work is important because it developed a comprehensive algorithm for homogenization of precipitation data, and the results provide better representation of precipitation climate and more robust estimates of precipitation trends. It also identified the causes for large biases in the published estimates of precipitation trends over Canada.

Open access
Heather MacDonald
,
Daniel W. McKenney
,
Xiaolan L. Wang
,
John Pedlar
,
Pia Papadopol
,
Kevin Lawrence
,
Yang Feng
, and
Michael F. Hutchinson

Abstract

This study presents spatial models (i.e., thin-plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5 day), and monthly time scales from 1900 to 2015. The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42 331 models for daily total precipitation and 1392 monthly total precipitation models, 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favorably to the corresponding unadjusted models. For example, root generalized cross-validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960–2003 period as compared with 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ~2–10-km resolutions.

Open access