Meeting twice with Hope, go through 2 additional papers
Assigned her one 2018 paper, will do the real data collecting practice (similar as Jason and Darlin), Hope will collect data into srdb, I will check her inputs, and Hope, Stephanie, and me will discuss this Wednesday.
Soil erosion (ESSD)
According to the similarity report, did some changes and resubmitted
Land model
Deadline for last round of comments is this Thursday
RC partitioning
Tang have shared me the N-deposition data, will work on this project this week
Global C
Updated to SRDB-V5
Kalyn will work on updating CMIP6 results
Rs(lit) is ~ 85 Pg, possible errors including:
1) Collar insertion
2) Monthly variability
3) Sites spatial uneven distribution
4) Measure frequency (from SRDB, the average is ~once a month), we can use COSORE to quantify the error of once per month?
5) Measure window (measured at day time, the average is 9-12 ?), we can use COSORE to quantify this error?
We can also use MGRsD to estimate mean monthly Rs for the global (i.e., we will have a global Rs estimate for Jan - Dec), and then get the global annual Rs
In this way, we do not have the error for 2), 4); but still need to consider error for 1), 2), and 5)
And finally, we want to see whether global annual Rs with error considered is closer to Rs(gpp)
I don't know whether we can (or is it necessary?) do a similar thing for GPP(lit)
SRDB updating
Soil erosion (ESSD)
Land model
RC partitioning
Global C
Updated to SRDB-V5
Kalyn will work on updating CMIP6 results
Rs(lit) is ~ 85 Pg, possible errors including: 1) Collar insertion 2) Monthly variability 3) Sites spatial uneven distribution 4) Measure frequency (from SRDB, the average is ~once a month), we can use COSORE to quantify the error of once per month? 5) Measure window (measured at day time, the average is 9-12 ?), we can use COSORE to quantify this error?![image](https://user-images.githubusercontent.com/13302161/97239734-bdc79500-17c2-11eb-985e-dc248dded089.png)
We can also use MGRsD to estimate mean monthly Rs for the global (i.e., we will have a global Rs estimate for Jan - Dec), and then get the global annual Rs
In this way, we do not have the error for 2), 4); but still need to consider error for 1), 2), and 5)
And finally, we want to see whether global annual Rs with error considered is closer to Rs(gpp)
I don't know whether we can (or is it necessary?) do a similar thing for GPP(lit)