December January February Research Articles

Effects of climate changes on dust aerosol over East Asia from RegCM3

In order to understand impacts of global warming on dust aerosol over East Asia, a regional climate model (RegCM3) coupled with a dust model is employed to simulate the present (1991–2000, following the observed concentration of the greenhouse gases) and future (2091–2100, following the A1B scenario) dust aerosol. Three experiments are performed over East Asia at a horizontal resolution of 50 km, driven by the outputs from a global model of the Model for Interdisciplinary Research on Climate (MIROC3.2_hires), two without (Exp.1 for the present and Exp.2 for the future) and one with (Exp.3 for the future) the radiative effects of dust aerosols. Effects of climate changes on dust aerosols and the feedback of radiative effects in the future are investigated by comparing differences of Exp.2 and Exp.1, Exp.3 and Exp.2, respectively. Results show that global warming will lead to the increases of dust emissions and column burden by 2% and 14% over East Asia, characterized by the increase in December–January–February–March (DJFM) and the decrease in April–May (AM). Similar variations are also seen in the projected frequencies of high dust emission events, showing an advanced active season of dust in the future. The net top-of-atmosphere (TOA) radiative forcing is positive over the desert source regions and negative over downwind regions, while the surface radiative forcing is negative over the domain, which will lead to a reduction of dust emissions and column burden.

Observed changes in extreme temperature and precipitation over Indonesia

ABSTRACTThis paper documents the changes in climate extremes over Indonesia during the past three decades (1983–2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days (TX10p) and cools nights (TN10p) had decreased whereas warm days (TX90p) and warm nights (TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum (TXmean) and minimum temperature (TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity (SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount (RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December–January–February (DJF) and/or March–April–May (MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June–July–August (JJA), September–October–November (SON) and MAM.

Eleven‐year solar cycle signal in the NAO and Atlantic/European blocking

The 11‐year solar cycle signal in December–January–February (DJF) averaged mean‐sea‐level pressure (SLP) and Atlantic/European blocking frequency is examined using multilinear regression with indices to represent variability associated with the solar cycle, volcanic eruptions, the El Niño–Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO). Results from a previous 11‐year solar cycle signal study of the period 1870–2010 (140 years; ∼13 solar cycles) that suggested a 3–4 year lagged signal in SLP over the Atlantic are confirmed by analysis of a much longer reconstructed dataset for the period 1660–2010 (350 years; ∼32 solar cycles). Apparent discrepancies between earlier studies are resolved and stem primarily from the lagged nature of the response and differences between early‐ and late‐winter responses. Analysis of the separate winter months provide supporting evidence for two mechanisms of influence, one operating via the atmosphere that maximises in late winter at 0–2 year lags and one via the mixed‐layer ocean that maximises in early winter at 3–4 year lags. Corresponding analysis of DJF‐averaged Atlantic/European blocking frequency shows a highly statistically significant signal at ∼1‐year lag that originates primarily from the late winter response. The 11‐year solar signal in DJF blocking frequency is compared with other known influences from ENSO and the AMO and found to be as large in amplitude and have a larger region of statistical significance.

The utility of seasonal hindcast database for the analysis of climate variability: an example

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December–January–February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill.

Sensitivity experiments of RegCM4 simulations to different convective schemes over Central Africa

In this study, version 4 of the International Centre for Theoretical Physics (ICTP) regional climate model named RegCM4 is used to perform 6 years simulation including 1 year for spin-up (from January 2001 to December 2006) over Central Africa using four convective schemes: The Emmanuel scheme (MIT), the Grell scheme with Arakawa-Schulbert closure assumption (GAS), the Grell scheme with Fritsch-Chappell closure assumption (GFC) and the Anthes-Kuo scheme (Kuo). We have investigated the ability of the model to simulate precipitation, surface temperature, wind and aerosols optical depth. Emphasis in the model results were made in December–January–February (DJF) and July–August–September (JAS) periods. Two subregions have been identified for more specific analysis namely: zone 1 which corresponds to the sahel region mainly classified as desert and steppe and zone 2 which is a region spanning the tropical rain forest and is characterized by a bimodal rain regime. We found that MIT scheme shows a better index of agreement in temperature, but it generally has a tendency to an overestimation of other simulated parameters. Overall, GAS scheme is more suitable to downscale the aforementioned parameters except temperature, as well as the diurnal cycle of precipitation everywhere over the study domain irrespective of the seasons. In JAS, model results are similar in the representation of regional wind circulation. Apart from the MIT scheme, all the convective schemes give the same tendencies in aerosols optical depth simulations. Additional experiment reveals that the use of BATS instead of Zeng scheme to calculate ocean flux appears to improve the quality of the model simulations.

Summer Arctic dipole wind pattern affects the winter Siberian High

This study investigates the relationship between the summer [June–July–August (JJA)] Arctic dipole wind pattern and the following winter [December–January–February (DJF)] Siberian High. It is found that the summer Arctic dipole wind pattern is not confined only to the Arctic region; it spans the large domain north of 20°N. The negative phase of this wind pattern depicts an anomalous anticyclone over the Arctic Ocean and its marginal seas, except for the Barents-Kara seas where an anomalous cyclone is dominant. This wind pattern is significantly correlated with the strength of the Siberian High during the following winter and with the frequency of extreme cold events over East Asia during the winters of 1979–2014. The relationship of this wind pattern with the winter Siberian High has strengthened over the past decades, particularly since the late 1980s. The more robust relationship coincides with significant changes in the winter atmospheric circulation and frequent occurrences of the negative phase of this wind pattern, which dynamically contributes to low September sea ice extent. The present study's results suggest that autumn Arctic sea ice provides a link between this wind pattern and climate variability over East Asia during the following winter. Results of simulation experiments suggest that (1) autumn sea ice loss favors the occurrence of a stronger East Asian winter monsoon; (2) the summer Arctic dipole wind pattern modulates winter atmospheric responses to sea ice loss, and the negative phase of this wind pattern enhances the negative feedback of Arctic sea ice loss on winter atmospheric variability over Eurasia and North America. These simulation experiments also imply that complex and varying summer circulation patterns obscure linkages between sea ice loss and large-scale circulation responses over Eurasia. Isolation of the summer Arctic dipole wind pattern, however, provides a potential precursor for the seasonal prediction of winter surface air temperature in a populous region of the world.

Predictable signals in seasonal mean soil moisture simulated with observation-based atmospheric forcing over China

The Community Land Model version 3.5 is driven by an observation-based meteorological dataset to simulate soil moisture over China for the period 1951–2008. A method for identifying the patterns of interannual variability that arise from slow (potentially predictable) and intraseasonal (unpredictable) variability is also applied; this allows identification of the sources of the predictability of seasonal soil moisture in China, during March–April–May (MAM), June–July–August (JJA), September–October–November (SON) and December–January–February (DJF). The potential predictability (slow-to-total) of the soil moisture above 1 m is high, with lowest value of 0.76 in JJA and highest value of 0.94 in DJF. The spatial distribution of the potential predictability comprises a northwest–southeast gradient, with a minimum center over East China and a maximum center over the northwest. The most important source of predictability is from the soil moisture persistence, which generally accounts for more than 50 % of the variability in soil moisture. The SSTs in the Indian Ocean, the North Atlantic and the eastern tropical Pacific Oceans are also identified as important sources of variability in the soil moisture, during MAM, JJA and SON/DJF, respectively. In addition, prolonged linear trends in each season are an important source. Using the slow principal component time series as predictands, a statistical scheme for the seasonal forecasting of soil moisture across China is developed. The prediction skills, in terms of the percentage of explained variance for the verification period (1992–2008), are 59, 51, 62 and 77 % during MAM–DJF, respectively. This is considerably higher than a normal grid prediction scheme.

Heavy Rainfall Distributions Over Java Sea in Wet Season

Mesoscale convective complexes (MCC) systems produce a wide variety of severe convective weather such as heavy rainfall. The MCC is a subclass of mesoscale convective systems (MCSs) defining as a convective cloud system that is large, long lived, and exhibits a quasi-circular cloud shield. This study evaluates spatial distributions of heavy rainfall over Java Sea region during wet season conditions; namely, December January February (DJF). Rainfall data set obtained from Tropical Rainfall Measuring Mission (TRMM) measurements for period 1998 - 2013. Heavy rainfall is represented by amount of maximum rainfall. Diurnal cycles of rainfall features present that the development and movement of convective rainfall is mainly related to the land and sea breezes. The evolution of hourly rainfall at 3 hourly intervals, illustrates a clear diurnal variation over Java Sea region. From 22 Local Time (LT) to 10 LT the large average rainfall mainly located in the Java Sea. Then from 13 LT to 19 LT the large average rainfall mainly located in the inland. While from 10 LT to 13 LT the large average rainfall located in both the Java Sea and the inland. And from 19 LT to 22 LT the large average rainfall located in Java coastwise. This situation has agreement with the formation of its maximum and standard deviation in diurnal cycles. The daily convective rainfall in the inland region is much more than that in the sea region, but heavy rainfall is often found over the sea.

Extreme Rainfall in Katulampa Associated with the Atmospheric Circulation

Extreme rainfall will increase the river flow and it can be triggered by regional and global atmospheric circulation. Objective of this study is to analyze the intensity of rainfall in Katulampa and its associated with the atmospheric circulation. We found that the extreme rainfall threshold in Katulampa is 67mm in December-January-February (DJF) which is determined using 95% percentile method. The number of extreme rainfall events is 86 days during 1981 to 2013. The results showed that increasing of rainfall intensity in Katulampa is influenced by the Madden-Julian Oscillation (MJO) and cold surge. MJO and cold surge modify the transport of water vapor and triggering the formation of convective clouds over the Java Island. Phase 2,3,4 and 5 of MJO have the highest impact on increasing rainfall intensity in Katulampa which is characterized by Outgoing Longwave Radiation (190 - 210W/m2). Meanwhile, the cold surge event has time lag about 1-2 days to increase the rainfall intensity in Katulampa.

The relationship between the subtropical Western Pacific SST and haze over North‐Central North China Plain

AbstractHaze is a serious issue in China today, presenting a substantial pollution problem to the public. To enhance understanding of haze mechanisms in recent years, this study examined the relationship between the subtropical western Pacific sea surface temperature (SST) and the haze pollution over North‐Central North China Plain. The empirical orthogonal function result showed an opposite trend of boreal winter (December–January–February) haze days (WHD) between the south and north of the Huaihe River. The negative sea surface temperature anomaly (SSTA) over the subtropical western Pacific could intensify the WHD over North‐Central North China Plain. As an effective external forcing, the preceding boreal autumn (Sepember–October–November) negative SSTA could weaken the East Asian winter monsoon (EAWM) circulation, leading to conditions such as a weaker Siberian High, reduced wind speed, higher humidity and an abnormal southerly wind. These conditions provide a favourable environment for haze with static stability and potential for hygroscopic growth. A series of SST sensitivity experiments were carried out using version 3.1 of the Community Atmosphere Model. When negative SSTA with different strengths were added into the model, all the key factors exhibited favourable features for haze, such as smaller EAWM index, stronger inversion potential, weaker vertical velocity, lower boundary layer height and higher relative humidity.

Homogeneous temperature series for thermal characterization and detection of trends in the Province of Santa Elena, Ecuador

ABSTRACTThe generation of quality climatic series is the first step in the analysis of climate characteristics and behaviour in any region of the world. This article approaches the construction of homogeneous average temperature series for the period 1982–2011, for the study of spatial distribution and temporal evolution in the Province of Santa Elena, in the most western point of continental Ecuador in South America. Despite the low variability registered in the temperatures of the Province, these were higher and more homogeneous in the inland region than on the coast and almost 3 °C higher in the trimesters of the rainy season December–January–February (DJF) and March–April–May (MAM) than in the trimesters of the dry season June–July–August (JJA) and September–October–November (SON). Trend analysis detected positive increasing rates for the temperature of the province, with values three times higher during DJF and MAM in comparison with SON and annual average trend of 0.33 °C decade−1 in the region. The construction of homogeneous average temperature series in the Province of Santa Elena has been an important step towards the spatial‐temporal characterization of this peculiar climatic zone. These series can also be employed as base scenarios for the development of climate change studies in the region.

Changes in the distribution of cold waves in France in the middle and end of the 21st century with IPSL-CM5 and CNRM-CM5 models

In this paper, a stochastic model is used to simulate daily minimum temperature time series coming from observations and two CMIP5 climate models (IPSL-CM5A-MR and CNRM-CM5) in order to analyze the changes in cold wave number and proportions under future climate conditions. The stochastic model allows computing 100 temperature time series for each different source (observation or climate model), and for 22 locations in France, which enables inferring the statistical significance of the changes. Two future time periods, near (around 2010–2060) and far future (around 2050–2100), and two RCPs (RCP4.5 and RCP8.5) are considered, while 3 different thresholds are used to identify cold waves: 0 °C and the 10th and 5th percentiles of observed wintertime (December–January–February) daily minimum temperature distribution. The results show that both models project a significantly lower number of cold waves in the future, all durations considered, but the changes mainly concern the proportion of the longest cold waves (10 days and more). The decreases are higher with IPSL-CM5A-MR than with CNRM-CM5. The main driver of this change is the decreasing frequency of the observation based thresholds in the future, which is higher for IPSL-CM5-MR model because the impact of a higher mean is enhanced by a decrease in the variance.

Regional climate model projections of the South Pacific Convergence Zone

This study presents results from regional climate model (RCM) projections for the south-west Pacific Ocean. The regional models used bias corrected sea surface temperatures. Six global climate models (GCMs) were used to drive a global variable resolution model on a quasi-uniform 60 km grid. One of these simulations was used to drive three limited area regional models. Thus a four member ensemble was produced by different RCMs downscaling the same GCM (GFDL2.1), and a six member ensemble was produced by the same RCM (Conformal Cubic Atmospheric Model—CCAM) downscaling six different GCMs. Comparison of the model results with precipitation observations shows the differences to be dominated by the choice of RCM, with all the CCAM simulations performing similarly and generally having lower error than the other RCMs. However, evaluating aspects of the model representation of the South Pacific Convergence Zone (SPCZ) does not show CCAM to perform better in this regard. In terms of the future projections of the SPCZ for the December–January–February season, the ensemble showed no consensus change in most characteristics though a majority of the ensemble members project a decrease in the SPCZ strength. Thus, similar to GCM based studies, there is large uncertainty concerning future changes in the SPCZ and there is no evidence to suggest that future changes will be outside the natural variability. These RCM simulations do not support an increase in the frequency of zonal SPCZ events.

Season of birth is associated with birth weight, pubertal timing, adult body size and educational attainment: a UK Biobank study.

Season of birth, a marker of in utero vitamin D exposure, has been associated with a wide range of health outcomes. Using a dataset of ∼450,000 participants from the UK Biobank study, we aimed to assess the impact of this seasonality on birth weight, age at menarche, adult height and body mass index (BMI). Birth weight, age at menarche and height, but not BMI, were highly significantly associated with season of birth. Individuals born in summer (June–July–August) had higher mean birth weight (P = 8 × 10−10), later pubertal development (P = 1.1 × 10−45) and taller adult height (P = 6.5 × 10−9) compared to those born in all other seasons. Concordantly, those born in winter (December–January–February) showed directionally opposite differences in these outcomes. A secondary comparison of the extreme differences between months revealed higher odds ratios [95% confidence intervals (CI)] for low birth weight in February vs. September (1.23 [1.15–1.32], P = 4.4 × 10−10), for early puberty in September vs. July (1.22 [1.16–1.28], P = 7.3 × 10−15) and for short stature in December vs. June (1.09 [1.03–1.17], P = 0.006). The above associations were also seen with total hours of sunshine during the second trimester, but not during the first three months after birth. Additional associations were observed with educational attainment; individuals born in autumn vs. summer were more likely to continue in education post age 16 years (P = 1.1 × 10−91) or attain a degree-level qualification (P = 4 × 10−7). However, unlike other outcomes, an abrupt difference was seen between those born in August vs. September, which flank the start of the school year. Our findings provide support for the ‘fetal programming’ hypothesis, refining and extending the impact that season of birth has on childhood growth and development. Whilst other mechanisms may contribute to these associations, these findings are consistent with a possible role of in utero vitamin D exposure.

Trends in extreme temperature over Nigeria from percentile‐based threshold indices

ABSTRACTThis study analyses spatial and temporal trends in extreme temperature indices over Nigeria. The percentile indices were calculated from newly homogenized daily minimum and maximum temperature data for the period 1971–2012 for 21 stations in Nigeria. Indices describing the characteristics of hot extremes and cold extremes are calculated with the RClimDex software. The annual and seasonal trends in these indices are obtained using the ordinary least square fit and the statistical significance tested using the R‐based modified Mann–Kendall test. We examine characteristics of these indices for the entire country and separately for the three geographical zones in Nigeria: Guinea, Savanna, and Sahel. The spatial and temporal patterns of trends in the indices indicate that Nigeria has experienced statistically significant increase in the frequency of hot extreme and decrease in cold extreme events. Although majority of the stations have significant trends in warm days and warm nights, the annual trend is greatest in warm nights. In addition, the rate of warming in minimum temperature (‘warm nights’) is stronger in June, July, August (JJA) and September, October, November (SON) compare with December, January, February (DJF) and March, April, May (MAM). As for the trends in cold days and cold nights, the trends in cold nights are larger than for cold days at both the annual and seasonal scales. The regional analysis indicates that trends in warm nights and cold nights are most pronounced in Guinea and Sahel regions.

Seasonal precipitation forecast skill over Iran

This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble (NMME) models, and two coupled ocean–atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature (SST) anomalies forecasted using constructed analogue SSTs (ECHAM4.5-SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 (ECHAM4.5-MOM3-DC2) and the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5° × 0.5° spatial resolution of the Global Precipitation Climatology Centre (GPCC) Version 6 gridded precipitation data, using model output statistics (MOS) developed through the canonical correlation analysis (CCA) option of the Climate Predictability Tool (CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic (ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models' skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation – the October-November-December (OND) season. For most seasons, a high skill is obtained from ECHAM4.5-MOM3-DC2 forecasts at a 1-month lead time when the models' 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January (NDJ), December-January-February (DJF) and January-February-March (JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectively.

The seasonal variations in the significant wave height and sea surface wind speed of the China’s seas

Long-term variations in a sea surface wind speed (WS) and a significant wave height (SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation, and other activities. The seasonal characteristics of the long-term trends in China’s seas WS and SWH are determined based on 24 a (1988–2011) cross-calibrated, multi-platform (CCMP) wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III (WW3) wave model forced by CCMP wind data. The results show the following. (1) For the past 24 a, the China’s WS and SWH exhibit a significant increasing trend as a whole, of 3.38 cm/(s·a) in the WS, 1.3 cm/a in the SWH. (2) As a whole, the increasing trend of the China’s seas WS and SWH is strongest in March-April-May (MAM) and December-January-February (DJF), followed by June-July-August (JJA), and smallest in September-October-November (SON). (3) The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China’s seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gulf, and the Gulf of Thailand exhibited a significant increase in all seasons. (4) The variations in China’s seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF.

Simulation of the interannual variations of tropospheric ozone over China: Roles of variations in meteorological parameters and anthropogenic emissions

Abstract We quantify the interannual variations (IAVs) of tropospheric O3 over China for the years 2004–2012 by using the one-way nested-grid version of the global three-dimensional Goddard Earth Observing System chemical transport model (GEOS-Chem). The roles of variations in meteorological fields and anthropogenic emissions of O3 precursors are examined separately and together through sensitivity simulations. With variations in both meteorological parameters and emissions, simulated seasonal mean surface-layer O3 concentrations over North China (NC, 110–120°E, 32–42°N) exhibit the largest IAVs in June–July–August (JJA). The regionally averaged absolute percent departure from the mean (APDM) values over NC are 0.7%, 3.2%, 3.9%, and 2.1% in December–January–February (DJF), March–April–May (MAM), and September–October–November (SON), respectively. Over South China (SC, 110–120°E, 22–32°N), the IAVs of O3 are found maximum in MAM, and minimum in JJA; the APDM values are 2.7%, 3.7%, 1.4%, and 2.6% in DJF, MAM, JJA, and SON, respectively. With respect to the IAVs of O3 over the Sichuan Basin (SCB, 102–110°E, 27–33°N), the APDM values are simulated to be 2.7–3.8% throughout the year. The IAVs in surface-layer O3 by variations in meteorological fields are simulated to be larger than those by variations in anthropogenic emissions throughout the year in NC and SC except for JJA in SC. The relatively more important role of variations in anthropogenic emissions is simulated in SCB in all seasons. Process analyses are performed to identify key meteorological parameters that influence the IAVs of O3 over NC, SC, and SCB. Over all of these regions, variations in winds are found to have the largest impact on the IAVs of O3, followed by those in temperature and specific humidity. Considering that the APDM values represent the IAVs averaged over 2004–2012, the magnitudes of IAVs of O3 for specific years can be more significant than the numbers reported here. Our results have important implications for the effectiveness of short-term air quality control strategies in China.

Diagnosing the seasonal land–atmosphere correspondence over northern Australia: dependence on soil moisture state and correspondence strength definition

Abstract. The similarity of the temporal variations of land and atmospheric states during the onset (September) through to the peak (February) of the wet season over northern Australia is statistically diagnosed using ensembles of offline land surface model simulations that produce a range of different background soil moisture states. We derive the temporal correspondence between variations in the soil moisture and the planetary boundary layer via a statistical measure of rank correlation. The simulated evaporative fraction and the boundary layer are shown to be strongly correlated during both SON (September–October–November) and DJF (December–January–February) despite the differing background soil moisture states between the two seasons and among the ensemble members. The sign and magnitude of the boundary layer–surface layer soil moisture association during the onset of the wet season (SON) differs from the correlation between the evaporative fraction and boundary layer from the same season, and from the correlation between the surface soil moisture and boundary layer association during DJF. The patterns and magnitude of the surface flux–boundary layer correspondence are not captured when the relationship is diagnosed using the surface layer soil moisture alone. The conflicting results arise because the surface layer soil moisture lacks strong correlation with the atmosphere during the monsoon onset because the evapotranspiration is dominated by transpiration. Our results indicate that accurately diagnosing the correspondence and therefore coupling strength in seasonally dry regions, such as northern Australia, requires root zone soil moisture to be included.

Decadal variability of global ocean significant wave height

This paper presents the long-term climate changes of significant wave height (Hs) in 1958–2001 over the entire global ocean using the 45-year European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) wave data. The linear trends in Hs and regional and seasonal differences of the linear trends for Hs were calculated. Results show that the Hs exhibits a significant increasing trend of about 4.6 cm decade-1 in the global ocean as a whole over the last 44 years. The Hs changes slowly during the periods 1958–1974 and 1980–1991, while it increases consistently during the periods 1975–1980 and 1995–1998. The Hs reaches its lowest magnitude in 1975, with annual average wave height about 2 m. In 1992, the Hs has the maximum value of nearly 2.60 m. The Hs in most ocean waters has a significant increasing trend of 2–14 cm decade-1 over the last 44 years. The linear trend exhibits great regional differences. Areas with strong increasing trend of Hs are mainly distributed in the westerlies of the southern Hemisphere and the northern Hemisphere. Only some small areas show obvious decreasing in Hs. The long-term trend of Hs in DJF (December, January, February) and MAM (March, April, May) is much more stronger than that in JJA (June, July, August) and SON (September, October, November). The linear trends of the Hs in different areas are different in different seasons; for instance, the increasing trend of Hs in the westerlies of the Pacific Ocean mainly appears in MAM and DJF.