How predictable is the northern hemisphere summer upper-tropospheric circulation?

The retrospective forecast skill of three coupled climate models (NCEP CFS, GFDL CM2.1, and CAWCR POAMA 1.5) and their multi-model ensemble (MME) is evaluated, focusing on the Northern Hemisphere (NH) summer upper-tropospheric circulation along with surface temperature and precipitation for the 25-year period of 1981–2005. The seasonal prediction skill for the NH 200-hPa geopotential height basically comes from the coupled models’ ability in predicting the first two empirical orthogonal function (EOF) modes of interannual variability, because the models cannot replicate the residual higher modes. The first two leading EOF modes of the summer 200-hPa circulation account for about 84% (35.4%) of the total variability over the NH tropics (extratropics) and offer a hint of realizable potential predictability. The MME is able to predict both spatial and temporal characteristics of the first EOF mode (EOF1) even at a 5-month lead (January initial condition) with a pattern correlation coefficient (PCC) skill of 0.96 and a temporal correlation coefficient (TCC) skill of 0.62. This long-lead predictability of the EOF1 comes mainly from the prolonged impacts of El Niño-Southern Oscillation (ENSO) as the EOF1 tends to occur during the summer after the mature phase of ENSO. The second EOF mode (EOF2), on the other hand, is related to the developing ENSO and also the interdecadal variability of the sea surface temperature over the North Pacific and North Atlantic Ocean. The MME also captures the EOF2 at a 5-month lead with a PCC skill of 0.87 and a TCC skill of 0.67, but these skills are mainly obtained from the zonally symmetric component of the EOF2, not the prominent wavelike structure, the so-called circumglobal teleconnection (CGT) pattern. In both observation and the 1-month lead MME prediction, the first two leading modes are accompanied by significant rainfall and surface air temperature anomalies in the continental regions of the NH extratropics. The MME’s success in predicting the EOF1 (EOF2) is likely to lead to a better prediction of JJA precipitation anomalies over East Asia and the North Pacific (central and southern Europe and western North America).     

Correlation of atmospheric visibility with chemical composition of Kaohsiung aerosols

The objective of this study was to investigate the correlation of visibility with chemical composition of Kaohsiung aerosols. Daytime visibility was observed around noon at two observation sites in metropolitan Kaohsiung, Taiwan in the years of 1999 and 2000. Both seasonal and diurnal variation patterns of visibility were observed in the region. Ambient aerosols were sampled and analyzed for 11 constituents, including water-soluble ionic species (Cl⁻, NO₃⁻, SO₄⁻², NH₄⁺, K⁺, Na⁺, Ca⁺², and Mg⁺²) and carbonaceous contents (OC, EC, and TC), to characterize the chemical composition of Kaohsiung aerosols. Furthermore, a stepwise multiple linear regression model was developed to elucidate the influence of aerosol species on visibility impairments. The results showed that sulfate was the dominant species that affected both light scattering coefficient and visibility. On average, the percentage contributions of visibility degrading species to light scattering coefficient were 29% for sulfate, 28% for nitrate, 22% for total carbon, and 21% for PM_2.5-remainder. An empirical regression model of visibility based on sulfate, nitrate, and relative humidity was also developed. The model showed that sulfate in PM_2.5 was the most sensitive species to visibility variation, suggesting that the reduction of sulfate in PM_2.5 could effectively improve the visibility of metropolitan Kaohsiung. During the investigation period, an event of Asian dusts intruded metropolitan Kaohsiung and dramatically increased the aerosol loadings, especially in the coarse particles. However, local visual air quality did not degrade accordingly during the Asian dust event because both visibility and light scattering coefficient are affected mainly by the fine particles. The results are discussed in detail in the paper.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

Distribution of seasonal rainfall in the East Asian monsoon region

Summary ?This study deals with the climatological aspect of seasonal rainfall distribution in the East Asian monsoon region, which includes China, Korea and Japan. Rainfall patterns in these three countries have been investigated, but little attention has been paid to the linkages between them. This paper has contributed to the understanding of the inter-linkage of various sub-regions. Three datasets are used. One consists of several hundred gauges from China and South Korea. The second is based on the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP). The two sources of precipitation information are found to be consistent. The third dataset is the NCEP/NCAR reanalysis 850-hPa winds. The CMAP precipitation shows that the seasonal transition over East Asia from the boreal winter to the boreal summer monsoon component occurs abruptly in mid-May. From late March to early May, the spring rainy season usually appears over South China and the East China Sea, but it is not so pronounced in Japan. The summer monsoon rainy season over East Asia commonly begins from mid-May to late May along longitudes of eastern China, the Korean Peninsula, and Japan. A strong quasi-20-day sub-seasonal oscillation in the precipitation appears to be dominant during this rainy season. The end date of the summer monsoon rainy season in eastern China and Japan occurs in late July, while the end date in the Korean Peninsula is around early August. The autumn rainy season in the Korean Peninsula has a major range from mid-August to mid-September. In southern China, the autumn rainy season prevails from late August to mid-October but a short autumn rainy season from late August to early September is noted in the lower part of the Yangtze River. In Japan, the autumn rainy season is relatively longer from mid-September to late October. The sub-seasonal rainfall oscillation in Korea, eastern China and Japan are explained by, and comparable to, the 850-hPa circulation. The strong westerly frontal zone can control the location of the Meiyu, the Changma, and the Baiu in East Asia. The reason that the seasonal sea surface temperature change in the northwestern Pacific plays a critical role in the northward advance of the onset of the summer monsoon rainfall over East Asia is also discussed. Received October 5, 2001; revised April 23, 2002; accepted May 11, 2002     

Climate Change and Winter Wheat Management: A Modelling Scenario for South-Eastern England

Crop models are useful tools for assessing the impact of climate change on crop production. The dynamic crop-growth model, CERES-Wheat is used to examine crop management responses, including yield, under six climate change scenarios for the years 2025 and 2050 on the Estate of Imperial College at Wye, Kent, U.K. Sensitivity analysis shows a dry matter yield decrease in response to increases in temperature alone. CERES-Wheat was then constrained to assess the crop performance under water-limited production scenarios with different soils, and the results show that crop grain yield actually increases, largely due to CO₂ fertilisation leading to increased rates of photosynthesis. Different management practices (planting dates and nitrogen application) were applied to find the best adaptation strategies. In general, `early' sowing (10th September) had the highest simulated yield, and `late' sowing (10th November) the lowest. For the soils tested, the highest and sustained crop production was obtained from Hamble soils (silt loam) compared with either the Fyfield (sandy) or Denchworth (clay). Adding nitrogen and other fertilisers would likely be necessary to take full advantage of the CO₂ fertilisationeffect and to compensate, in some cases, for yield losses caused by climate change where water shortage becomes serious.     

Order classification of river watershed divides based on processing digital elevation models

The principles and the algorithm of order classification of river watershed divides are outlined. It is shown that a formal application of any available order classification procedures for river watershed divides is not theoretically grounded as well as being impractical, because the physical mechanisms for formation of watershed divide network are different from those operating in the river network. We have formulated the basic principle of determining the watershed divides order on the basis of a serial sequence of sections constituting the travel path of an arbitrary water drop from the watershed divide to the outlet section of the basin. It is suggested that the order N should be assigned to the section of the watershed divide belonging to the full divide line of the N-order basin only if the travel path from it to the N-order stream is “full” in the topological sense, i.e. includes sections of all orders, from 1 to N. Also, we present a variant of determining the order on the basis of so-called higher-order triplets, incomplete sequences of sections of three neighboring orders along the travel path, with the higher of them determining the watershed divide order. The use of triplets is a subjective procedure of generalization that eliminates the influence of recent random erosional incisions on the forms of stable high-order watersheds. We outline the variants of the technique for identifying the network of watershed divides and calculating their orders, based on processing the digital elevation models (DEM) through the use of standard GIS ArcMap tools. Results are obtained in the form of a correlated classification of river and watershed networks which are rationally interpreted and hold promise for investigating the structure, functioning and evolution of river systems. The study revealed the existence of formation “cores” of river systems, i.e. regions within which the system reaches a higher stream order and which are bounded by watersheds of the same order.     

The 1786 earthquake-triggered landslide dam and subsequent dam-break flood on the Dadu River, southwestern China — Reply

A short reply to discussion by Chanson.     

The differential geography of housing affordability in Sydney: a disaggregated approach

ABSTRACT

Despite extensive studies being devoted to housing affordability in Australia, few have investigated housing affordability at a disaggregated level. This is in spite of the fact that there are existing socio-economic and demographic disparities across different regions of a city. This study aims to fill this gap by examining housing affordability in Sydney, a city that is characterised by diverse demographic and socio-economic mix, from a sub-city perspective. Two dimensions of affordability are assessed from 1991 to 2016: entry-level and ongoing housing affordability. The study finds that entry-level housing remains extremely unaffordable in all regions of Greater Sydney, although the level of unaffordability varies across regions. Specifically, the deterioration in housing affordability is more obvious in low-income regions such as Western Sydney. In addition, the ongoing housing affordability of those who have entered the market improves considerably within 5–10 years, although there are significant variations between different regions. Importantly, residents in low-income regions such as Western Sydney take a longer period to improve their ongoing affordability. The findings of differential geography of housing affordability have some profound policy implications. Policymakers should consider the disparities across different regions by formulating a more targeted and regionally balanced housing policy.