The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for ...four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.
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.
The amplitude asymmetry between El Niño and La Niña is investigated by diagnosing the mixed-layer heat budget during the ENSO developing phase by using the three ocean assimilation products: Simple ...Ocean Data Assimilation (SODA) 2.0.2, SODA 1.4.2, and the Global Ocean Data Assimilation System (GODAS). It is found that the nonlinear zonal and meridional ocean temperature advections are essential to cause the asymmetry in the far eastern Pacific, whereas the vertical nonlinear advection has the opposite effect. The zonal current anomaly is dominated by the geostrophic current in association with the thermocline depth variation. The meridional current anomaly is primarily attributed to the Ekman current driven by wind stress forcing. The resulting induced anomalous horizontal currents lead to warm nonlinear advection during both El Niño and La Niña episodes and thus strengthen (weaken) the El Niño (La Niña) amplitude. The convergence (divergence) of the anomalous geostrophic mixed-layer currents during El Niño (La Niña) results in anomalous downwelling (upwelling) in the far eastern equatorial Pacific, which leads to a cold nonlinear vertical advection in both warm and cold episodes.
Given observed initial conditions, how well do coupled atmosphere-ocean models predict precipitation climatology with 1-month lead forecast? And how do the models' biases in climatology in turn ...affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981-2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models' ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian-Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.
Accurate prediction of the Asian-Australian monsoon (A-AM) seasonal variation is one of the most important and challenging tasks in climate prediction. In order to understand the causes of the low ...accuracy in the current prediction of the A-AM precipitation, this study strives to determine to what extent the ten state-of-the-art coupled atmosphere-ocean-land climate models and their multi-model ensemble (MME) can capture the two observed major modes of A-AM rainfall variability-which account for 43% of the total interannual variances during the retrospective prediction period of 1981-2001. The first mode is associated with the turnabout of warming to cooling in the El Niño-Southern Oscillation (ENSO), whereas the second mode leads the warming/cooling by about 1 year, signaling precursory conditions for ENSO. The first mode has a strong biennial tendency and reflects the Tropical Biennial Oscillation (Meehl in J Clim 6:31-41, 1993). We show that the MME 1-month lead prediction of the seasonal precipitation anomalies captures the first two leading modes of variability with high fidelity in terms of seasonally evolving spatial patterns and year-to-year temporal variations, as well as their relationships with ENSO. The MME shows a potential to capture the precursors of ENSO in the second mode about five seasons prior to the maturation of a strong El Niño. However, the MME underestimates the total variances of the two modes and the biennial tendency of the first mode. The models have difficulties in capturing precipitation over the maritime continent and the Walker-type teleconnection in the decaying phase of ENSO, which may contribute in part to a monsoon “spring prediction barrier” (SPB). The NCEP/CFS model hindcast results show that, as the lead time increases, the fractional variance of the first mode increases, suggesting that the long-lead predictability of A-AM rainfall comes primarily from ENSO predictability. In the CFS model, the correlation skill for the first principal component remains about 0.9 up to 6 months before it drops rapidly, but for the spatial pattern it exhibits a drop across the boreal spring. This study uncovered two surprising findings. First, the coupled models' MME predictions capture the first two leading modes of precipitation variability better than those captured by the ERA-40 and NCEP-2 reanalysis datasets, suggesting that treating the atmosphere as a slave may be inherently unable to simulate summer monsoon rainfall variations in the heavily precipitating regions (Wang et al. in J Clim 17:803-818, 2004). It is recommended that future reanalysis should be carried out with coupled atmosphere and ocean models. Second, While the MME in general better than any individual models, the CFS ensemble hindcast outperforms the MME in terms of the biennial tendency and the amplitude of the anomalies, suggesting that the improved skill of MME prediction is at the expense of overestimating the fractional variance of the leading mode. Other outstanding issues are also discussed.
Recent recovery of the Siberian High intensity Jeong, Jee-Hoon; Ou, Tinghai; Linderholm, Hans W. ...
Journal of Geophysical Research,
16 December 2011, Volume:
116, Issue:
D23
Journal Article
Peer reviewed
Open access
This study highlights the fast recovery of the wintertime Siberian High intensity (SHI) over the last two decades. The SHI showed a marked weakening trend from the 1970s to 1980s, leading to ...unprecedented low SHI in the early 1990s according to most observational data sets. This salient declining SHI trend, however, was sharply replaced by a fast recovery over the last two decades. Since the declining SHI trend has been considered as one of the plausible consequences of climate warming, the recent SHI recovery seemingly contradicts the continuous progression of climate warming in the Northern Hemisphere. We suggest that alleviated surface warming and decreased atmospheric stability in the central Siberia region, associated with an increase in Eurasian snow cover, in the recent two decades contributed to this rather unexpected SHI recovery. The prominent SHI change, however, is not reproduced by general circulation model (GCM) simulations used in the IPCC AR4. The GCMs indicate the steady weakening of the SHI for the entire 21st century, which is found to be associated with a decreasing Eurasian snow cover in the simulations. An improvement in predicting the future climate change in regional scale is desirable.
Key Points
The Siberian High intensity shows a conspicuous recovery trend since the 1990s
An increase in snow cover contributes to the Siberian High recovery
GCMs predict a weakening of Siberian High with a snow cover decrease for the 21C
ENSO nonlinearity in a warming climate Boucharel, J.; Dewitte, B.; du Penhoat, Y. ...
Climate dynamics,
11/2011, Volume:
37, Issue:
9-10
Journal Article
Peer reviewed
The El Niño Southern Oscillation (ENSO) is known as the strongest natural inter-annual climate signal, having widespread consequences on the global weather, climate, ecology and even on societies. ...Understanding ENSO variations in a changing climate is therefore of primordial interest to both the climate community and policy makers. In this study, we focus on the change in ENSO nonlinearity due to climate change. We first analysed high statistical moments of observed Sea Surface Temperatures (SST) timeseries of the tropical Pacific based on the measurement of the tails of their Probability Density Function (PDF). This allows defining relevant metrics for the change in nonlinearity observed over the last century. Based on these metrics, a zonal “see-saw” (oscillation) in nonlinearity patterns is highlighted that is associated with the change in El Niño characteristics observed in recent years. Taking advantage of the IPCC database and the different projection scenarios, it is showed that changes in El Niño statistics (or “flavour”) from a present-day climate to a warmer climate are associated with a significant change in nonlinearity patterns. In particular, in the twentieth century climate, the “conventional” eastern Pacific El Niño relates more to changes in nonlinearity than to changes in mean state whereas the central Pacific El Niño (or Modoki El Niño) is more sensitive to changes in mean state than to changes in nonlinearity. An opposite behaviour is found in a warmer climate, namely the decreasing nonlinearity in the eastern Pacific tends to make El Niño less frequent but more sensitive to mean state, whereas the increasing nonlinearity in the west tends to trigger Central Pacific El Niño more frequently. This suggests that the change in ENSO statistics due to climate change might result from changes in the zonal contrast of nonlinearity characteristics across the tropical Pacific.
El Niño in a changing climate Yeh, Sang-Wook; Kug, Jong-Seong; Dewitte, Boris ...
Nature (London),
09/2009, Volume:
461, Issue:
7263
Journal Article
Peer reviewed
El Niño events, characterized by anomalous warming in the eastern equatorial Pacific Ocean, have global climatic teleconnections and are the most dominant feature of cyclic climate variability on ...subdecadal timescales. Understanding changes in the frequency or characteristics of El Niño events in a changing climate is therefore of broad scientific and socioeconomic interest. Recent studies show that the canonical El Niño has become less frequent and that a different kind of El Niño has become more common during the late twentieth century, in which warm sea surface temperatures (SSTs) in the central Pacific are flanked on the east and west by cooler SSTs. This type of El Niño, termed the central Pacific El Niño (CP-El Niño; also termed the dateline El Niño, El Niño Modoki or warm pool El Niño), differs from the canonical eastern Pacific El Niño (EP-El Niño) in both the location of maximum SST anomalies and tropical-midlatitude teleconnections. Here we show changes in the ratio of CP-El Niño to EP-El Niño under projected global warming scenarios from the Coupled Model Intercomparison Project phase 3 multi-model data set. Using calculations based on historical El Niño indices, we find that projections of anthropogenic climate change are associated with an increased frequency of the CP-El Niño compared to the EP-El Niño. When restricted to the six climate models with the best representation of the twentieth-century ratio of CP-El Niño to EP-El Niño, the occurrence ratio of CP-El Niño/EP-El Niño is projected to increase as much as five times under global warming. The change is related to a flattening of the thermocline in the equatorial Pacific.
This study was conducted to investigate the feasibility of using yellow poplar as a structural member by determining allowable bending properties. Full-size lumber was classified by machine grading ...and the knot diameter ratio on the wide surface of lumber. The majority of machine grades of yellow poplar lumber with a cross-section of 38 × 89 mm2 were E8, E9, and E10. It was confirmed that the size of the knot diameter ratio tended to be smaller for higher machine grades. In the lowest grade, E8, of most machine grades, the allowable bending strength was lower than the corresponding design value in Korean standards. Application of 0.5 knot diameter ratio to the E8 grade lumber increased the bending strength to 3 MPa of the allowable value to suit the design value. All the allowable modulus of elasticities values of the majority of machine grades were higher than the design values. From the results of this study, it was expected that Korean yellow poplar could be utilized for structural bending members.
It has been recognized that the intensity of the east Asian (EA) summer monsoon has a negative correlation with that of the western North Pacific (WNP) summer monsoon. Here we show that this ...relationship is much stronger in the recent decade (1994–2004) than in the epoch before 1994 (1979–1993). The first two leading modes of summer‐mean precipitation over the large region of the WNP and EA region are shown to be associated with two factors: the ENSO development and the WNP summer monsoon fluctuation. The leading mode has changed from an ENSO‐related mode in 1979–1993 to a WNP summer monsoon‐related mode in the recent decade (1994–2004). The summer‐mean mid‐tropospheric geopotential heights that are correlated with the WNP monsoon index also show a marked change in the teleconnection (wave‐train) pattern between 1994–2004 and 1979–1993. All together this evidence suggests that the relationship between the EA and the WNP summer monsoons has experienced a significant decadal change around 1993–1994.
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