Estimation of intensity of tropical cyclone over Bay of Bengal using microwave imagery

caxky dh [kkM+h esa o"kZ 2008&2010 esa ,Q- Mh- ih- vof/k ¼15 vDrwcj ls 30 uoEcj½ ds nkSjku vk, ik¡p pØokrksa ds lw{e rjaxh; es?k fcEckofy;ksa rFkk 85 fxxkgV~tZ vko`fÙk esa izkIr fd, x, mRiknksa dh tk¡p dh xbZ gS ftlls rkieku nhfIr] rkieku nhfIr esa vfu;ferrk] dsUnz dk LFkku] lrg ij vuojr cgus okyk vf/kdre iou ¼,e- ,l- MCY;w-½ rFkk pØokrksa ds fHkUu&fHkUu fLFkfr;ksa esa muds rhozhdj.k ls lacaf/kr djdksa tSls% vonkc ¼Mh-½] xgu vonkc ¼Mh- Mh-½] pØokrh; rwQku ¼lh- ,l-½] rhoz pØokrh; rwQku ¼,l-lh-,l-½] vfr rhoz pØokrh; rwQku ¼oh-,l-lh-,l-½ vkfn dk vkdfyr dsUnzh; nkc ¼bZ- lh- ih-½ dk vkdyu fd;k tk ldsA izf{kr fd, x, nhfIr rkieku vfu;ferrkvksa dh rqyuk lS)kafrd :i ls bZ-lh-ih- ds csLV VªSd vkdyu ij vk/kkfjr nhfIr rkieku vfu;ferrk ,oa bu pØokrksa ds ckgjh nkc ds lkFk Hkh dh xbZ gSA dsUnz ds LFkku] bZ-lh-ih- ,oa lw{erajxh; fcEckoyh ds vk/kkj ij vkdfyr ,e- ,l- MCY;w- dh rqyuk csLV VªSd ,oa Hkkjr ekSle foKku foHkkx ds Mh- oksjkWd ds vkdyu ls dh xbZ gS vkSj mldk fo’ys"k.k fd;k x;k gSA pØokrh; fo{kksHk ¼lh- Mh-½ ds dsUnz ds LFkku esa varZ tSlkfd lw{erjaxh fcEckofy;ksa rFkk csLV VªSd vkdyu ds }kjk vkdfyr fd;k x;k gS] fo{kksHkksa ds rhozhdj.k ds lkFk&lkFk de gksrk tkrk gS vkSj vonkc ¼Mh-½ dh fLFkfr esa yxHkx 25 fd-eh- ls vfr rhoz pØokrh; rwQku ¼oh-,l-lh-,l-½ dh fLFkfr esa 18 fd- eh ds chp cnyrk jgrk gSA tcfd ;g varj Mh oksjkWd ds vkdyu ls dkQh vf/kd gSA lw{erjaxh; vkdyuksa ij vk/kkfjr ,e- ,l- MCY;w- vkdyu oh-,l- lh- ,l- ds nkSjku csLV VªSd vkdyuksa ls yxHkx 28 ukWV~l vf/kd vkdfyr fd;k x;k gS vkSj vonkc ¼Mh-½@pØokrh; rwQku ¼lh-,l-½@rhoz pØokrh; rwQku ¼,l- lh- ,l-½ dh fLFkfr esa ;g 6&8 ukWV~l vkdfyr fd;k x;k gSA csLV VSªd vkdyuksa ls lkisf{kd varj dks ns[kus ls irk pyk gS fd lh-,l- vkSj ,l-lh- dh fLFkfr esa lw{e rajx esa ,e-,l-MCY;w- yxHkx 12&15 izfr’kr vkSj oh-,l-lh-,l- dh fLFkfr esa yxHkx 30 izfr’kr vf/kd vkdfyr gqvk gS tcfd Mh- oksjkWd dk ,e- ,l- MCY;w- vkdyu lh- ,l-] ,l- lh- ,l- vkSj oh- ,l- lh- ,l- dh fLFkfr;ksa esa 15&18 izfr’kr de gks x;k gSA caxky dh [kkM+h ds Åij 230 dsfYou dk nhfIr rkieku vonkc ds cuus ds fy, vuqdwy gksrk gS] 250 dsfYou dk rkieku bldks pØokrh rwQku esa 260 dsfYou rhoz pØokrh rwQku esa vkSj 270 dsfYou vfr izpaM+ pØokrh rwQku esa cny nsrk gSA nhfIr rkieku ds nsgyheku ¼FkszlksYM osY;w½ ds vfHkKku ¼fMVSD’ku½ ls bl iz.kkyh ds rhoz gksus dk iwokZuqeku nsus ds fy, iz;kIr vfxze le; fey ldrk gSA blh izdkj nhfIr rkieku folaxfr 3 dsfYou ls vf/kd gksus ij pØokrh; rwQku rhoz pØokrh; rwQku esa cny tkrk gS vkSj 8 dsfYou dk rkieku bls caxky dh [kkM+h esa vfr izpaM pØokrh; rwQku ds :i esa cny nsrk gSA Microwave cloud imageries and derived products in the frequency of 85 GHz have been examined for five cyclones that occurred during FDP period (15 October- 30 November) of 2008-2010 over the Bay of Bengal to estimate the brightness temperature, brightness temperature anomaly, location of centre, maximum sustained wind (MSW) at surface level and estimated central pressure (ECP) associated with cyclones in their different stages of intensification like depression (D), deep depression (DD), cyclonic storm (CS), severe cyclonic storm (SCS), very severe cyclonic storm (VSCS), etc. Also the observed brightness temperature anomalies are compared with theoretically derived brightness temperature anomalies based on the best track estimates of ECP and outermost pressure for these cyclones. The location of centre, ECP and MSW based on microwave imagery estimates have been compared with those available from the best track and Dvorak’s estimates of India Meteorological Department and analyzed. The difference in location of the centre of cyclonic disturbance (CD) as estimated by microwave imageries and best track estimates decreases with intensification of the disturbances and varies from about 25 km in depression (D) stage to 18 km in VSCS stage whereas the difference is significantly higher in case of Dvorak estimate compared to best track estimate. The MSW based on microwave estimates is higher than that of best track estimates by about 28 knots during VSCS and 6-8 knots during D, CS, SCS stage. Considering relative difference with respect to best track estimates, the MSW is overestimated in microwave by about 12-15% in case of CS and SCS stage and by about 30% in VSCS stage while Dvorak’s MSW overestimation reduced to 15-18% during CS, SCS and VSCS stages. Brightness temperature of the order of 230 K is favourable for genesis (formation of D), 250K for its intensification into CS, 260 K for intensification into SCS and 270K for its further intensification into VSCS stage over the Bay of Bengal. Detection of threshold value of brightness temperature may provide adequate lead time to forecast intensification of the system. Similarly, when brightness temperature anomaly exceeds 3K, CS intensify into SCS and 8K, it intensifies into a VSCS over Bay of Bengal.

Siberian 2020 heatwave increased spring CO2 uptake but not annual CO2 uptake

Siberia experienced an unprecedented strong and persistent heatwave in winter to spring of 2020. Using bottom-up and top-down approaches, we evaluated seasonal and annual CO2 fluxes of 2020 in the northern hemisphere (north of 30ºN), focusing on Siberia where the pronounced heatwave occurred. We found that over Siberia, CO2 respiration loss in response to the pronounced positive winter temperature anomaly was greater than in previous years. However, continued warming in spring enhanced photosynthetic CO2 uptake, resulting in the largest seasonal transition in NEE, that is, the largest magnitude of the switch from the net CO2 loss in winter to net CO2 uptake in spring until June. However, this exceptional transition was followed by the largest reduction in CO2 uptake in late summer, due to multiple environmental constraints, including a soil moisture deficit. Despite a substantial increase of CO2 uptake by 22 ± 9 gC m-2 in spring in response to the heatwave, the mean annual CO2 uptake over Siberia was slightly lower (3 ± 13 gC m-2 year-1) than the average of the previous five years. These results highlight the highly dynamic response of seasonal carbon fluxes to extreme temperature anomalies at high latitudes, indicating a seasonal compensation between abnormal uptake and release of CO2 in response to extreme warmth that may limit carbon sink capacity in high northern latitudes.

1206. Effects of Climatic Variability on Lyme Disease Outbreaks in California

Background Climate change has increased the risk of tick borne infections. The life cycle and prevalence of deer ticks are strongly influenced by temperature. Warmer temperatures associated with climate change are projected to increase the range of suitable tick habitats driving the spread of Lyme disease (LD). Short winters could also increase tick activity increasing the risk of exposure. This study examines the relationship between LD incidence and temperature-precipitation and their anomalies in CA counties. Methods Trends and relationships of Lyme Disease (LD) cases and climatic factors were analyzed among the California counties from 2000 to 2019. Lyme disease tabulate data and climatic data were obtained from Centers for Disease Control, and NOAA, and Climate Data Guide respectively. Canonical correspondence analysis (CCA) was performed using variables: (i) LD cases, (ii) precipitation & anomaly, and temperature & anomaly. The CCA ordination explained the variability between LD cases and climatic variables. Biplots were used to visualize the associations between LD cases and climatic anomalies. Results We compared the countywide LD cases in relation to climatic factors in California from 2000 to 2019. A total of 96 cases in 2000, 117 cases in 2009, and 144 cases in 2019 were reported in the 55 counties of California. Santa Clara reported the highest LD cases in 2003 (23 cases; 16%), followed by Los Angeles in 2013 (20 cases; 18%) and Santa Cruz in 2017 (19 cases; 13%). CCA ordination showed distinguishable clustering patterns between southern California counties (Santa Clara, Santa Cruz, Alameda, and San Diego) and northern coast and Klamath mountains range (Humboldt, Trinity, Shasta, and Siskiyou) regions (Fig. 1). Moderate mean annual temperature (56.5 °F - 62.5 °F) and temperature anomaly (3.8 °F - 5.5 °F) were the most important variable predictor for high LD outbreak. The CCA ordination shows the relationships between Lyme Disease and climatic variables for the 55 Counties of California. The bottom right circle represents Lyme cases positively correlated with temperature anomaly (3.8 °F - 5.5 °F) and moderate annual mean temperature (56.5 °F - 62.5 °F). The upper left circle represents Lyme cases negatively correlated with mean annual precipitation. Conclusion Moderate temperature with low moist spell anomalies in the south neighboring CA counties showed a positive influence on LD outbreak. The climatic conditions in those areas suitable for Oak trees and masting acorn resulting in the establishment of tick and host (deer) populations. We recommend robust surveillance and lab testing for patients with a history of tick bites in these regions. Disclosures All Authors: No reported disclosures

The 3D Structure of Northern Hemisphere Blocking Events: Climatology, Role of Moisture, and Response to Climate Change

To better understand the dynamics and impacts of blocking events, their 3D structure needs to be further investigated. We present a comprehensive composite analysis of the 3D structure of blocks and its response to future climate change over North Pacific, North Atlantic, and Russia in summers and winters using reanalysis and two large-ensemble datasets from CESM1 and GFDLCM3. In reanalysis, over both ocean and land, the anomalous winds are equivalent-barotropic in the troposphere and stratosphere, and temperature anomalies are positive throughout the troposphere and negative in the lower stratosphere. The main seasonal and regional differences are that blocks are larger/stronger in winters; over oceans, the temperature anomaly is shifted westward due to latent heating. Analyzing the temperature tendency equation shows that in all three sectors, adiabatic warming due to subsidence is the main driver of the positive temperature anomaly; however, depending on season and region, meridional thermal advection and latent heating might have leading-order contributions too. Both GCMs are found to reproduce the climatological 3D structure remarkably well, but sometimes disagree on future changes. Overall, the future summertime response is weakening of all fields (except for specific humidity), although the impact on near-surface temperature is not necessarily weakened; e.g., the blocking-driven near-surface warming over Russia intensifies. The wintertime response is strengthening of all fields, except for temperature in some cases. Responses of geopotential height and temperature are shifted westward in winters, most likely due to latent heating. Results highlight the importance of process-level analyses of blocks’ 3D structure for improved understanding of the resulting temperature extremes and their future changes.