Purpose We compared the efficacy of ofatumumab (O) versus rituximab (R) in combination with cisplatin, cytarabine, and dexamethasone (DHAP) salvage treatment, followed by autologous stem-cell ...transplantation (ASCT) in patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL). Patients and Methods Patients with CD20
DLBCL age ≥ 18 years who had experienced their first relapse or who were refractory to first-line R-CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone)-like treatment were randomly assigned between three cycles of R-DHAP or O-DHAP. Either O 1,000 mg or R 375 mg/m
was administered for a total of four infusions (days 1 and 8 of cycle 1; day 1 of cycles 2 and 3 of DHAP). Patients who experienced a response after two cycles of treatment received the third cycle, followed by high-dose therapy and ASCT. Primary end point was progression-free survival (PFS), with failure to achieve a response after cycle 2 included as an event. Results Between March 2010 and December 2013, 447 patients were randomly assigned. Median age was 57 years (range, 18 to 83 years); 17% were age ≥ 65 years; 63% had stage III and IV disease; 71% did not achieve complete response (CR) or experience response for < 1 year on first-line R-CHOP. Response rate for O-DHAP was 38% (CR, 15%) versus 42% (CR, 22%) for R-DHAP. ASCT on protocol was completed by 74 patients (33%) in the O arm and 83 patients (37%) in the R arm. PFS, event-free survival, and overall survival were not significantly different between O-DHAP versus R-DHAP: PFS at 2 years was 24% versus 26% (hazard ratio HR, 1.12; 95% CI, 0.89 to 1.42; P = .33); event-free survival at 2 years was 16% versus 18% (HR, 1.10; P = .35); and overall survival at 2 years was 41% versus 38% (HR, 0.90; P = .38). Positron emission tomography negativity before ASCT was highly predictive for superior outcome. Conclusion No difference in efficacy was found between O-DHAP and R-DHAP as salvage treatment of relapsed or refractory DLBCL.
The impact of sampling variability on the correlation between all-India rainfall (AIR) and the El Niño-Southern Oscillation is investigated in a large ensemble of seasonal climate simulations made ...using the European Centre for Medium-Range Weather Forecasting Ensemble Prediction System at T319 (64 km). The analyzed runs consist of 51 ensemble members initialized each May 1 for the period 1980–2011 and integrated for 7 months. 10,000 pairs of 32-year timeseries of June–September (JJAS) mean AIR and NINO3 indices are created from this database by randomly drawing one of the 51 ensemble members for each year. The correlation between each pair of AIR and NINO3 series is then calculated, generating a distribution of AIR–NINO3 correlation values. The model is reinitialized with observations each May 1 and thus all members are drawn from the same background state by construction and any differences in correlation are attributable to sampling variability. The spread in the calculated correlation values and the differences between 32-year segments are sufficient to explain the observed variations in AIR–NINO3 correlation since the beginning of the 1900s, including the sharp decrease in correlation strength since the late 1970s. Sampling variability thus represents a strong null hypothesis for the observed changes and one that cannot be rejected at the 95 % level based on our simulations.
Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration ...between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the ACE and, to a lesser extent, the TC frequency, even though the influence of large-scale climate variations on these TC activity measures is largely independent of resolution changes. The biggest gain occurs in transition from T319 to T639. Significant skill in regional TC forecasts is achieved over broad areas of the Northern Hemisphere. The highest-resolution hindcasts exhibit additional locations with skill in the NA and EP, including land-adjacent areas. The feasibility of regional intensity forecasts is assessed. In the presence of the coupled model biases, the benefits of high resolution for seasonal TC forecasting may be underestimated.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The climate system model of the National Center for Atmospheric Research is used to examine the predictability arising from the land surface initialization of seasonal climate ensemble forecasts in ...current, preindustrial, and projected future settings. Predictability is defined in terms of the model’s ability to predict its own interannual variability. Predictability from the land surface in this model is relatively weak compared to estimates from other climate models but has much of the same spatial and temporal structure found in previous studies. Several factors appear to contribute to the weakness, including a low correlation between surface fluxes and subsurface soil moisture, less soil moisture memory (lagged autocorrelation) than other models or observations, and relative insensitivity of the atmospheric boundary layer to surface flux variations. Furthermore, subseasonal cyclical behavior in plant phenology for tropical grasses introduces spurious unrealistic predictability at low latitudes during dry seasons. Despite these shortcomings, intriguing changes in predictability are found. Areas of historical land use change appear to have experienced changes in predictability, particularly where agriculture expanded dramatically into the Great Plains of North America, increasing land-driven predictability there. In a warming future climate, land–atmosphere coupling strength generally increases, but added predictability does not always follow; many other factors modulate land-driven predictability.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 (CCSM4) for preindustrial, present, and future climate to study the effects of ...realistic land surface initializations on subseasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at subseasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the preindustrial climate, although the spatial average changes for North America were rather small (<10%). To attribute the potential cause of changes in land‐driven temperature predictability, they are correlated with the changes in land related climate metrics. The changes in temperature predictability are positively (0.40), and negatively (−0.35) correlated with the changes in nonrainy days evaporative fraction, and changes in dryness index respectively. From this result, the hypothesis arises that wetter conditions favor higher land‐driven temperature predictability in North America. We tested the hypothesis by rearranging the predictability experiment ensembles and found support for the hypothesis in the midlatitude regions and short‐term forecasts (16 to 30 days).
Key Points
Realistic land surface initializations affect subseasonal climate forecastsGlobal warming: significant spatial changes in temperature predictabilityLand surface contribution is higher during wet years
Changes in the atmospheric response to SST variability in the decade 2065–75 are estimated from time-slice-like experiments using the NCAR Community Atmosphere Model, version 3 (CAM3) AGCM forced by ...specified SST and external forcing. The current climate is simulated using observed monthly SST and external forcing for 1951–2000. The change in mean SST for the future is represented by the difference between the 2065–75 and 1965–75 decadal mean SST climatologies from coupled model twentieth-century/future climate simulations of the response to external forcing. The change in external forcing is similarly specified as the change of the external forcing concurrent with the SST change. These seasonally varying changes in SST and external forcing are added to the 50-year sequence of 1951–2000 observed SST and external forcings to produce the specified future climate forcings for the AGCM.
Changes in the December through February mean ENSO teleconnections are evaluated from the difference between ensemble means from future and current climate time slice simulations. The ENSO teleconnections are strengthened and displaced westward in the time slice simulations, which is not in agreement with CGCM projections. These changes are associated with increased precipitation/atmospheric heating anomalies due to the warmer tropical SST. The quasigeostrophic stationary wave activity flux indicates that the dominant cause of the changes is a southward shift in a midlatitude central Pacific wave activity source rather than changes in the basic-state stationary wave dispersion properties.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A considerable number of authors presented experiments in which degradation of large scale circulation occurred in regional climate integrations when large-scale nudging was not used (e.g., von ...Storch et al., 2000; Biner et al., 2000; Rockel et al., 2008; Sanchez-Gomez et al., 2008; Alexandru et al., 2009; among others). We here show an earlier 9-member ensemble result of the June-August precipitation difference over the contiguous United States between the "flood year" of 1993 and the "drought year" of 1988, in which the Eta model nested in the COLA AGCM gave a rather accurate depiction of the analyzed difference, even though the driver AGCM failed in doing so to the extent of having a minimum in the area where the maximum ought to be. It is suggested that this could hardly have been possible without an RCM's improvement in the large scales of the driver AGCM. We further revisit the issue by comparing the large scale skill of the Eta RCM against that of a global ECMWF 32-day ensemble forecast used as its driver. Another issue we are looking into is that of the lateral boundary condition (LBC) scheme. The question we ask is whether the almost universally used but somewhat costly relaxation scheme is necessary for a desirable RCM performance? We address this by running the Eta in two versions differing in the lateral boundary scheme used. One of these is the traditional relaxation scheme and the other is the Eta model scheme in which information is used at the outermost boundary only and not all variables are prescribed at the outflow boundary. The skills of these two sets of RCM forecasts are compared against each other and also against that of their driver. A novelty in our experiments is the verification used. In order to test the large scale skill we are looking at the forecast position accuracy of the strongest winds at the jet stream level, which we have taken as 250 hPa. We do this by calculating bias adjusted equitable threat scores (Mesinger 2008) and frequency bias scores for wind speeds greater than a chosen wind speed threshold, with the ECMWF analyses used as truth. We also calculate a traditional RMS difference between the forecast and analyzed winds at this same level. Our results show the Eta RCM skill in forecasting large scales with no interior nudging to be just about the same as and usually even slightly higher than that of the driver model. As to the LBC impact, no disadvantage compared to relaxation was seen from using the Eta scheme, in spite of its requiring information from the outermost RCM boundary only.
Systematic biases in U.S. summer integrations with the Center for Ocean‐Land‐Atmosphere Studies (COLA) atmospheric general circulation model (GCM) have been identified and analyzed. Positive surface ...air temperature biases of 2°–4°K occurred over the central United States. The temperature biases were coincident with the agricultural region of the central United States, where negative precipitation biases also occurred. The biases developed in June and became very significant during July and August. The impact of the crop area vegetation and soil properties on the biases was investigated in a series of numerical experiments. The biases were largely caused by the erroneous prescription of crop vegetation phenology in the surface model of the GCM. The prescribed crop soil properties also contributed to the biases. On the basis of these results the crop model has been improved and the systematic errors in the U.S. summer simulations have been reduced. The numerical experiments also revealed that land surface effects on the atmospheric variables at and near the surface during the North American summer are very pronounced and persistent but are largely limited to the area of the anomalous land surface forcing. In this regard, the midlatitude land surface effects described here are similar to those previously found for tropical regions.
Ensemble integrations of three general circulation models (Center for Ocean–Land–Atmosphere Studies, NCAR, and NCEP) have been performed over five different boreal summer seasons (June through ...September of 1986–88 and 1993–94) with prescribed observed sea surface temperature to assess the predictability of seasonal climate during the boreal summer. Beyond some inconsistent initialization of soil wetness among the models, there is no land surface contribution to predictability that can be assessed. The models show a rapid degradation of skill in global terrestrial surface temperature after the first month, and no skill in precipitation over land. Potential predictability is assessed by examining in tandem the models’ skill as measured by their anomaly correlation coefficients, and the models’ signal-to-noise ratio (essentially interannual versus intraensemble variance) as a measure of confidence in the results. Collocation of skill in anomaly simulation and a robust signal is a strong indicator of potential predictability. Predictability of interannual climate variations is found to be low outside the deep Tropics, and nil over land. With only SST as a driving boundary condition, the poor performance of these models during summer may indicate that one must turn to the land surface in order to harvest potential predictability.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The National Centers for Environmental Prediction (NCEP) Eta Model (80 km, 38L) is used to simulate the tropical South American summer (January–March) climate for 1983, 1985, 1987, 1989, and 1991 ...using lateral boundary conditions from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis. Simulations of the lower tropospheric circulation and precipitation are analyzed to study the variability on diurnal, intraseasonal, and interannual timescales. The results are compared with observations and previous studies.
The Eta Model produces better regional circulation details, such as low-level jets (LLJs), than does the reanalysis because of its higher resolution, more realistic topography and coastal geometry, and because of its ability to realistically simulate the effects of mesoscale circulation on the time-mean flow. The model detects not only the LLJ east of the Andes Mountains and the LLJ west of northern Cordillera Occidental, which have been reported in previous studies, but it also detects three distinct LLJs just north of the equator embedded in the strong northeasterly trade winds over Colombia, Venezuela, and Guiana. All the LLJs show strong diurnal variability with a nocturnal maximum. The LLJ east of the Andes Mountains brings warm moist air from the Amazon basin to the Gran Chaco region where the jet exits. The moisture convergence in the jet exit region creates favorable conditions for precipitation. Hence, the precipitation over the region also shows strong diurnal variability with a nocturnal maximum. The LLJs just north of the equator bring moisture from the tropical Atlantic Ocean, the western Caribbean Sea, and the Gulf of Panama to their exit regions over the northern Amazon basin and west coasts of Colombia and Ecuador. The precipitation over these regions also has diurnal variability with a nocturnal maximum. The diurnal variability of precipitation over most of the Tropics has an afternoon rainfall maximum except for regions influenced by LLJs, which have a nocturnal rainfall maximum. The intraseasonal variability of the LLJs is episodic with an approximate period of 20 days. The interannual variability of the LLJs is dominated by the ENSO cycle. The LLJ east of the Andes Mountains is stronger in the warm phase of ENSO than in the cold phase. However, the model has some difficulty simulating the observed relationship between the strength of LLJ and precipitation, but the model succeeds in the case of LLJs just north of the equator. For example, these LLJs are weaker in the warm phase of ENSO than in the cold phase. Hence, during the warm (cold) phase of ENSO, dry (wet) conditions normally occur over the northern part of the Amazon basin, which is the exit region of these LLJs.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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