SIPNET errors not being caught

[serbinsh]

Describe the bug It looks like SIPNET runs will continue through then workflow without throwing an error even if no sipnet.clim file is created/available and all model runs fail without producing output.

prerun
Error reading from climate file: first location must be loc. 0
mv: cannot stat `/data/Model_Output/pecan.output/PEcAn_2000000989/run/2000075060/sipnet.out': No such file or directory

> # Do conversions
> settings <- PEcAn.utils::do_conversions(settings)
2018-08-17 11:00:43 DEBUG  [PEcAn.utils::do_conversions] : 
   do.conversion outdir /data/Model_Output/pecan.output/dbfiles 
2018-08-17 11:00:43 INFO   [PEcAn.utils::do_conversions] : PROCESSING:  met 
2018-08-17 11:00:43 INFO   [PEcAn.utils::do_conversions] : 
   calling met.process: 
2018-08-17 11:00:44 INFO   [browndog.met] : 
   browndog download url : 
   http://dap.ncsa.illinois.edu:8184/file/20278776_pecan.clim 
2018-08-17 11:00:56 WARN   [PEcAn.DB::dbfile.input.insert] : 
   Multiple input files found matching parameters format_id = 24, startdate 
   = 2004/01/01, enddate = 2004/12/31, parent = .  Selecting the last input 
   file.  This is normal for when an entire ensemble is inserted 
   iteratively, but is likely an error otherwise. 
2018-08-17 11:00:56 WARN   [PEcAn.DB::db.close] : 
   Connection created outside of PEcAn.DB package 
2018-08-17 11:00:56 DEBUG  [PEcAn.utils::do_conversions] : 
   updated met path: 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
> 
> # Query the trait database for data and priors
> if (PEcAn.utils::status.check("TRAIT") == 0){
+   PEcAn.utils::status.start("TRAIT")
+   settings <- PEcAn.workflow::runModule.get.trait.data(settings)
+   PEcAn.settings::write.settings(settings, outputfile='pecan.TRAIT.xml')
+   PEcAn.utils::status.end()
+ } else if (file.exists(file.path(settings$outdir, 'pecan.TRAIT.xml'))) {
+   settings <- PEcAn.settings::read.settings(file.path(settings$outdir, 'pecan.TRAIT.xml'))
+ }
2018-08-17 11:00:58 WARN   [dbfile.check] : 
   Multiple Valid Files found on host machine. Returning last updated 
   record. 
2018-08-17 11:00:58 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:58 INFO   [query.trait.data] : Amax 
2018-08-17 11:00:58 INFO   [query.trait.data] : Median Amax : 8.395 
2018-08-17 11:00:58 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:58 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:58 INFO   [query.trait.data] : leafC 
2018-08-17 11:00:58 INFO   [query.trait.data] : Median leafC : 50.55 
2018-08-17 11:00:58 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:58 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:58 INFO   [query.trait.data] : SLA 
2018-08-17 11:00:59 INFO   [query.trait.data] : Median SLA : 6.7 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : leaf_turnover_rate 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   Median leaf_turnover_rate : 0.286 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   leaf_respiration_rate_m2 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   Median leaf_respiration_rate_m2 : 1.05 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : root_turnover_rate 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   Median root_turnover_rate : 0.515 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   root_respiration_rate 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   Median root_respiration_rate : 5.75 
2018-08-17 11:00:59 INFO   [query.trait.data] : 
   --------------------------------------------------------- 
2018-08-17 11:00:59 INFO   [FUN] : 
   Summary of Prior distributions for: temperate.needleleaf.evergreen 
2018-08-17 11:00:59 INFO   [FUN] : 
   distn parama paramb n 
2018-08-17 11:00:59 INFO   [FUN] : 
   root_respiration_rate lnorm 2.0700 0.4000 39 
2018-08-17 11:00:59 INFO   [FUN] : 
   growth_resp_factor beta 4.0600 7.2000 0 
2018-08-17 11:00:59 INFO   [FUN] : 
   leaf_turnover_rate unif 0.0400 1.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   root_turnover_rate unif 0.0000 10.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   Amax unif 0.0000 40.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   leaf_respiration_rate_m2 weibull 2.0000 6.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   SLA lnorm 1.8900 0.6100 455 
2018-08-17 11:00:59 INFO   [FUN] : 
   leafC norm 50.6000 1.3200 291 
2018-08-17 11:00:59 INFO   [FUN] : 
   Vm_low_temp norm 0.0000 3.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   stem_respiration_rate unif 0.0000 100.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   extinction_coefficient unif 0.3800 0.6200 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   veg_respiration_Q10 unif 1.4000 2.6000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   fine_root_respiration_Q10 unif 1.4000 5.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   coarse_root_respiration_Q10 unif 1.4000 5.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   fracLeafFall unif 0.0010 0.1000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   leaf_allocation_fraction beta 8.9103 18.9848 0 
2018-08-17 11:00:59 INFO   [FUN] : 
   root_allocation_fraction beta 15.2563 19.7635 0 
2018-08-17 11:00:59 INFO   [FUN] : 
   psnTOpt norm 30.0000 8.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   leafGrowth norm 120.0000 25.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   wood_allocation_fraction beta 5.0000 40.0000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   half_saturation_PAR norm 16.0000 4.5000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   dVPDSlope norm 0.1500 0.0400 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   dVpdExp norm 2.3000 0.4000 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   AmaxFrac norm 0.5000 0.1200 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   wood_turnover_rate norm 0.5000 0.1200 NA 
2018-08-17 11:00:59 INFO   [FUN] : 
   number of observations per trait for temperate.needleleaf.evergreen 
2018-08-17 11:01:00 INFO   [FUN] : 
   28 observations of Amax 
2018-08-17 11:01:00 INFO   [FUN] : 
   12 observations of leafC 
2018-08-17 11:01:00 INFO   [FUN] : 
   47 observations of SLA 
2018-08-17 11:01:00 INFO   [FUN] : 
   97 observations of leaf_turnover_rate 
2018-08-17 11:01:00 INFO   [FUN] : 
   10 observations of leaf_respiration_rate_m2 
2018-08-17 11:01:00 INFO   [FUN] : 
   6 observations of root_turnover_rate 
2018-08-17 11:01:00 INFO   [FUN] : 
   213 observations of root_respiration_rate 
> 
> 
> # Run the PEcAn meta.analysis
> if(!is.null(settings$meta.analysis)) {
+   if (PEcAn.utils::status.check("META") == 0){
+     PEcAn.utils::status.start("META")
+     PEcAn.MA::runModule.run.meta.analysis(settings)
+     PEcAn.utils::status.end()
+   }
+ }
2018-08-17 11:01:00 INFO   [FUN] : 
   ------------------------------------------------------------------- 
2018-08-17 11:01:00 INFO   [FUN] : 
   Running meta.analysis for PFT: temperate.needleleaf.evergreen 
2018-08-17 11:01:00 INFO   [FUN] : 
   ------------------------------------------------------------------- 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  Amax data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   Amax P[X<x] = 0.209875 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  leafC data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   leafC P[X<x] = 0.484892162865082 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  SLA data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   SLA P[X<x] = 0.507917847637762 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  leaf_turnover_rate data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   leaf_turnover_rate P[X<x] = 0.356770833333333 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  leaf_respiration_rate_m2 data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   leaf_respiration_rate_m2 P[X<x] = 0.0301608054092977 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  root_turnover_rate data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   root_turnover_rate P[X<x] = 0.0515 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   OK!  root_respiration_rate data and prior are consistent: 
2018-08-17 11:01:01 INFO   [check_consistent] : 
   root_respiration_rate P[X<x] = 0.211237093649205 
Each meta-analysis will be run with: 
3000 total iterations,
4 chains, 
a burnin of 1500 samples,
, 
thus the total number of samples will be 6000
################################################
------------------------------------------------
starting meta-analysis for:

 Amax 

------------------------------------------------
prior for Amax
                     (using R parameterization):
unif(0, 40)
data max: 25.69 
data min: 1.7845 
mean: 10.3 
n: 28
stem plot of data points

  The decimal point is at the |

   0 | 88
   2 | 37
   4 | 0
   6 | 523466
   8 | 1235556
  10 | 0
  12 | 55
  14 | 0
  16 | 6
  18 | 3939
  20 | 
  22 | 
  24 | 7

stem plot of obs.prec:

  The decimal point is 2 digit(s) to the right of the |

  0 | 00000000000000125
  2 | 
  4 | 
  6 | 5

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 46
   Unobserved stochastic nodes: 13
   Total graph size: 149

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

              Mean      SD  Naive SE Time-series SE
beta.ghs[2] -8.044 1.90240 0.0245599      0.0245655
beta.o      16.838 0.17548 0.0022654      0.0025288
sd.y         3.833 0.04699 0.0006066      0.0006758

2. Quantiles for each variable:

               2.5%    25%    50%    75%  97.5%
beta.ghs[2] -11.809 -9.353 -8.037 -6.744 -4.303
beta.o       16.497 16.716 16.840 16.956 17.181
sd.y          3.742  3.801  3.833  3.865  3.927

################################################
------------------------------------------------
starting meta-analysis for:

 leafC 

------------------------------------------------
prior for leafC
                     (using R parameterization):
norm(50.6, 1.32)
data max: 53.7 
data min: 47.7 
mean: 50.5 
n: 12
stem plot of data points

  The decimal point is at the |

  46 | 7
  48 | 349
  50 | 55691
  52 | 387

stem plot of obs.prec:

  The decimal point is 3 digit(s) to the right of the |

  0 | 00000000002
  0 | 
  1 | 
  1 | 
  2 | 
  2 | 5

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 24
   Unobserved stochastic nodes: 2
   Total graph size: 49

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

          Mean      SD  Naive SE Time-series SE
beta.o 50.4789 0.18167 0.0023453      0.0024102
sd.y    0.8932 0.04439 0.0005731      0.0006147

2. Quantiles for each variable:

          2.5%     25%    50%    75%   97.5%
beta.o 50.1207 50.3574 50.480 50.602 50.8393
sd.y    0.8096  0.8628  0.892  0.923  0.9835

################################################
------------------------------------------------
starting meta-analysis for:

 SLA 

------------------------------------------------
prior for SLA
                     (using R parameterization):
lnorm(1.89, 0.61)
data max: 17.7 
data min: 2.73 
mean: 7.42 
n: 47
stem plot of data points

  The decimal point is at the |

   2 | 78023345555559
   4 | 01590178
   6 | 077889134
   8 | 0673
  10 | 07
  12 | 67748
  14 | 344
  16 | 87

stem plot of obs.prec:

  The decimal point is 3 digit(s) to the right of the |

  0 | 000000000000000000000012224
  1 | 1
  2 | 
  3 | 
  4 | 
  5 | 
  6 | 
  7 | 
  8 | 6

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 76
   Unobserved stochastic nodes: 21
   Total graph size: 220

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

             Mean      SD  Naive SE Time-series SE
beta.ghs[2] 4.519 0.20918 0.0027005      0.0034978
beta.o      5.329 0.14280 0.0018436      0.0025926
sd.y        1.155 0.02063 0.0002663      0.0002715

2. Quantiles for each variable:

             2.5%   25%   50%   75% 97.5%
beta.ghs[2] 4.100 4.378 4.522 4.662 4.924
beta.o      5.056 5.235 5.326 5.423 5.615
sd.y        1.116 1.141 1.155 1.169 1.197

################################################
------------------------------------------------
starting meta-analysis for:

 leaf_turnover_rate 

------------------------------------------------
NO ERROR STATS PROVIDED, DROPPING RANDOM EFFECTS
prior for leaf_turnover_rate
                     (using R parameterization):
unif(0.04, 1)
data max: 0.769 
data min: 0.12625 
mean: 0.392 
n: 15
stem plot of data points

  The decimal point is 1 digit(s) to the left of the |

  0 | 3
  2 | 3777928
  4 | 12569
  6 | 37

no estimates of SD for leaf_turnover_rate
Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 15
   Unobserved stochastic nodes: 17
   Total graph size: 79

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

         Mean      SD  Naive SE Time-series SE
beta.o 0.3319 0.03543 0.0004574      0.0005033
sd.y   0.3446 0.07013 0.0009053      0.0009175

2. Quantiles for each variable:

         2.5%    25%    50%    75%  97.5%
beta.o 0.2625 0.3086 0.3317 0.3551 0.4009
sd.y   0.2382 0.2950 0.3344 0.3823 0.5112

################################################
------------------------------------------------
starting meta-analysis for:

 leaf_respiration_rate_m2 

------------------------------------------------
prior for leaf_respiration_rate_m2
                     (using R parameterization):
weibull(2, 6)
data max: 1.8 
data min: 0.583410488985368 
mean: 1.13 
n: 10
stem plot of data points

  The decimal point is at the |

  0 | 679
  1 | 00134
  1 | 58

stem plot of obs.prec:

  The decimal point is 3 digit(s) to the right of the |

  0 | 000000
  0 | 
  1 | 
  1 | 
  2 | 
  2 | 55

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 18
   Unobserved stochastic nodes: 4
   Total graph size: 58

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

         Mean      SD  Naive SE Time-series SE
beta.o 1.4089 0.02606 0.0003364      0.0003648
sd.y   0.3382 0.01115 0.0001439      0.0001523

2. Quantiles for each variable:

         2.5%    25%   50%    75%  97.5%
beta.o 1.3570 1.3920 1.409 1.4265 1.4596
sd.y   0.3173 0.3304 0.338 0.3458 0.3608

################################################
------------------------------------------------
starting meta-analysis for:

 root_turnover_rate 

------------------------------------------------
prior for root_turnover_rate
                     (using R parameterization):
unif(0, 10)
data max: 0.98 
data min: 0.42 
mean: 0.597 
n: 6
stem plot of data points

  The decimal point is 1 digit(s) to the left of the |

  4 | 2376
  6 | 2
  8 | 8

stem plot of obs.prec:

  The decimal point is 4 digit(s) to the right of the |

  2 | 
  3 | 00000
  4 | 
  5 | 
  6 | 7

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 12
   Unobserved stochastic nodes: 2
   Total graph size: 35

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

         Mean      SD  Naive SE Time-series SE
beta.o 0.6033 0.03222 0.0004160      0.0004345
sd.y   0.1308 0.00961 0.0001241      0.0001301

2. Quantiles for each variable:

        2.5%    25%    50%    75%  97.5%
beta.o 0.540 0.5816 0.6038 0.6249 0.6666
sd.y   0.113 0.1241 0.1305 0.1370 0.1507

################################################
------------------------------------------------
starting meta-analysis for:

 root_respiration_rate 

------------------------------------------------
prior for root_respiration_rate
                     (using R parameterization):
lnorm(2.07, 0.4)
data max: 79.8 
data min: 0.0967276868352595 
mean: 10.6 
n: 155
stem plot of data points

  The decimal point is 1 digit(s) to the right of the |

  0 | 000000000000111111111222222233333333333334444444444444444
  0 | 55555555555555666666666666777777778888888899999999
  1 | 000001111122223333444
  1 | 55555667799
  2 | 4
  2 | 8
  3 | 3
  3 | 67
  4 | 004
  4 | 78
  5 | 03
  5 | 9
  6 | 
  6 | 9
  7 | 0
  7 | 
  8 | 0

stem plot of obs.prec:

  The decimal point is 4 digit(s) to the right of the |

  0 | 00000000000000000000000000000000000000000000000000000000000000000000+12
  0 | 
  1 | 
  1 | 
  2 | 
  2 | 
  3 | 0
  3 | 
  4 | 3

Read 28 items
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 249
   Unobserved stochastic nodes: 64
   Total graph size: 670

Initializing model

Iterations = 1002:4000
Thinning interval = 2 
Number of chains = 4 
Sample size per chain = 1500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

               Mean       SD Naive SE Time-series SE
beta.ghs[2] -10.161 0.188756 0.002437      0.0031825
beta.o       16.925 0.125865 0.001625      0.0022876
sd.y          2.785 0.008909 0.000115      0.0001216

2. Quantiles for each variable:

               2.5%     25%     50%     75%  97.5%
beta.ghs[2] -10.538 -10.288 -10.162 -10.031 -9.798
beta.o       16.681  16.841  16.925  17.008 17.167
sd.y          2.768   2.779   2.785   2.791  2.803

2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  Amax data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   Amax P[X<x] = 0.420986386298642 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  leafC data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   leafC P[X<x] = 0.464082969352723 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  SLA data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   SLA P[X<x] = 0.360807169707833 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  leaf_turnover_rate data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   leaf_turnover_rate P[X<x] = 0.303062792560165 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  leaf_respiration_rate_m2 data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   leaf_respiration_rate_m2 P[X<x] = 0.0536449161761655 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  root_turnover_rate data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   root_turnover_rate P[X<x] = 0.0603745744319219 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   OK!  root_respiration_rate data and prior are consistent: 
2018-08-17 11:01:11 INFO   [check_consistent] : 
   root_respiration_rate P[X<x] = 0.971077239543006 
2018-08-17 11:01:12 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen Amax GD MPSRF = 
   1 
2018-08-17 11:01:12 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen leafC GD MPSRF = 
   1 
2018-08-17 11:01:12 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen SLA GD MPSRF = 
   1.002 
2018-08-17 11:01:13 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen 
   leaf_turnover_rate GD MPSRF = 1.001 
2018-08-17 11:01:13 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen 
   leaf_respiration_rate_m2 GD MPSRF = 1.003 
2018-08-17 11:01:13 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen 
   root_turnover_rate GD MPSRF = 1.001 
2018-08-17 11:01:14 INFO   [pecan.ma.summary] : 
   JAGS model converged for temperate.needleleaf.evergreen 
   root_respiration_rate GD MPSRF = 1.001 
> 
> # Write model specific configs
> if (PEcAn.utils::status.check("CONFIG") == 0){
+   PEcAn.utils::status.start("CONFIG")
+   settings <- PEcAn.workflow::runModule.run.write.configs(settings)
+   PEcAn.settings::write.settings(settings, outputfile='pecan.CONFIGS.xml')
+   PEcAn.utils::status.end()
+ } else if (file.exists(file.path(settings$outdir, 'pecan.CONFIGS.xml'))) {
+   settings <- PEcAn.settings::read.settings(file.path(settings$outdir, 'pecan.CONFIGS.xml'))
+ }
2018-08-17 11:01:17 INFO   [PEcAn.uncertainty::get.parameter.samples] : 
   Selected PFT(s): temperate.needleleaf.evergreen 
Warning in rm(prior.distns, post.distns, trait.mcmc) :
  object 'prior.distns' not found
Warning in rm(prior.distns, post.distns, trait.mcmc) :
  object 'post.distns' not found
Warning in rm(prior.distns, post.distns, trait.mcmc) :
  object 'trait.mcmc' not found
2018-08-17 11:01:17 INFO   [PEcAn.uncertainty::get.parameter.samples] : 
   PFT temperate.needleleaf.evergreen has MCMC samples for: Amax leafC SLA 
   leaf_turnover_rate leaf_respiration_rate_m2 root_turnover_rate 
   root_respiration_rate 
2018-08-17 11:01:17 INFO   [PEcAn.uncertainty::get.parameter.samples] : 
   PFT temperate.needleleaf.evergreen will use prior distributions for: 
   growth_resp_factor Vm_low_temp stem_respiration_rate 
   extinction_coefficient veg_respiration_Q10 fine_root_respiration_Q10 
   coarse_root_respiration_Q10 fracLeafFall leaf_allocation_fraction 
   root_allocation_fraction psnTOpt leafGrowth wood_allocation_fraction 
   half_saturation_PAR dVPDSlope dVpdExp AmaxFrac wood_turnover_rate 
2018-08-17 11:01:18 INFO   [PEcAn.uncertainty::get.parameter.samples] : 
   using 5004 samples per trait 
2018-08-17 11:01:18 INFO   [get.ensemble.samples] : 
   Using uniform random sampling 
Loading required package: PEcAn.SIPNET
2018-08-17 11:01:19 INFO   [write.config.SIPNET] : 
   Writing SIPNET configs with input 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
2018-08-17 11:01:19 INFO   [write.config.SIPNET] : 
   Writing SIPNET configs with input 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
2018-08-17 11:01:19 WARN   [write.config.SIPNET] : 
   Sum of allocation parameters exceeds 1 for runid = 2000075061 - This 
   won't break anything since SIPNET has internal check, but notice that 
   such combinations might not take effect in the outputs. 
2018-08-17 11:01:19 INFO   [write.config.SIPNET] : 
   Writing SIPNET configs with input 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
2018-08-17 11:01:19 INFO   [write.config.SIPNET] : 
   Writing SIPNET configs with input 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
2018-08-17 11:01:19 INFO   [write.config.SIPNET] : 
   Writing SIPNET configs with input 
   /data/Model_Output/pecan.output/dbfiles/BD-b99e48b494b5/sipnet.clim 
2018-08-17 11:01:19 INFO   [PEcAn.workflow::run.write.configs] : 
   ###### Finished writing model run config files ##### 
2018-08-17 11:01:19 INFO   [PEcAn.workflow::run.write.configs] : 
   config files samples in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/run 
2018-08-17 11:01:19 INFO   [PEcAn.workflow::run.write.configs] : 
   parameter values for runs in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/samples.RData 
> 
> if ((length(which(commandArgs() == "--advanced")) != 0) && (PEcAn.utils::status.check("ADVANCED") == 0)) {
+   PEcAn.utils::status.start("ADVANCED")
+   q();
+ }
> 
> # Start ecosystem model runs
> if (PEcAn.utils::status.check("MODEL") == 0) {
+   PEcAn.utils::status.start("MODEL")
+   PEcAn.remote::runModule.start.model.runs(settings,stop.on.error=FALSE)
+   PEcAn.utils::status.end()
+ }
2018-08-17 11:01:19 INFO   [start.model.runs] : 
   ------------------------------------------------------------------- 
2018-08-17 11:01:19 INFO   [start.model.runs] : 
   Starting model runs SIPNET 
2018-08-17 11:01:19 INFO   [start.model.runs] : 
   ------------------------------------------------------------------- 

  |                                                                            
  |                                                                      |   0%2018-08-17 11:01:19 DEBUG  [start_qsub] : 
   qsub -l walltime=36:00:00 -V -N PEcAn-2000075060 -o 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075060/stdout.log 
   -e 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075060/stderr.log 
   -S /bin/bash 
2018-08-17 11:01:19 DEBUG  [start_qsub] : 
   Running locally: 2000075060 
2018-08-17 11:01:19 DEBUG  [start.model.runs] : 
   JOB.SH submit status: 148007.modex.bnl.gov 
2018-08-17 11:01:19 DEBUG  [start_qsub] : 
   qsub -l walltime=36:00:00 -V -N PEcAn-2000075061 -o 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075061/stdout.log 
   -e 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075061/stderr.log 
   -S /bin/bash 
2018-08-17 11:01:19 DEBUG  [start_qsub] : 
   Running locally: 2000075061 
2018-08-17 11:01:19 DEBUG  [start.model.runs] : 
   JOB.SH submit status: 148008.modex.bnl.gov 
2018-08-17 11:01:19 DEBUG  [start_qsub] : 
   qsub -l walltime=36:00:00 -V -N PEcAn-2000075062 -o 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075062/stdout.log 
   -e 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075062/stderr.log 
   -S /bin/bash 
2018-08-17 11:01:20 DEBUG  [start_qsub] : 
   Running locally: 2000075062 
2018-08-17 11:01:20 DEBUG  [start.model.runs] : 
   JOB.SH submit status: 148009.modex.bnl.gov 
2018-08-17 11:01:20 DEBUG  [start_qsub] : 
   qsub -l walltime=36:00:00 -V -N PEcAn-2000075063 -o 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075063/stdout.log 
   -e 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075063/stderr.log 
   -S /bin/bash 
2018-08-17 11:01:20 DEBUG  [start_qsub] : 
   Running locally: 2000075063 
2018-08-17 11:01:20 DEBUG  [start.model.runs] : 
   JOB.SH submit status: 148010.modex.bnl.gov 
2018-08-17 11:01:20 DEBUG  [start_qsub] : 
   qsub -l walltime=36:00:00 -V -N PEcAn-2000075064 -o 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075064/stdout.log 
   -e 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075064/stderr.log 
   -S /bin/bash 
2018-08-17 11:01:20 DEBUG  [start_qsub] : 
   Running locally: 2000075064 
2018-08-17 11:01:20 DEBUG  [start.model.runs] : 
   JOB.SH submit status: 148011.modex.bnl.gov 

2018-08-17 11:01:20 DEBUG  [start.model.runs] : 
   Waiting for the following jobs: 148007 148008 148009 148010 148011 
qstat: Unknown Job Id Error 148007.modex.bnl.gov
2018-08-17 11:02:32 DEBUG  [qsub_run_finished] : 
   Job 148007 for run 148007 finished 

  |                                                                            
  |==============                                                        |  20%qstat: Unknown Job Id Error 148008.modex.bnl.gov
2018-08-17 11:02:32 DEBUG  [qsub_run_finished] : 
   Job 148008 for run 148008 finished 

  |                                                                            
  |============================                                          |  40%qstat: Unknown Job Id Error 148009.modex.bnl.gov
2018-08-17 11:02:32 DEBUG  [qsub_run_finished] : 
   Job 148009 for run 148009 finished 

  |                                                                            
  |==========================================                            |  60%qstat: Unknown Job Id Error 148010.modex.bnl.gov
2018-08-17 11:02:32 DEBUG  [qsub_run_finished] : 
   Job 148010 for run 148010 finished 

  |                                                                            
  |========================================================              |  80%qstat: Unknown Job Id Error 148011.modex.bnl.gov
2018-08-17 11:02:32 DEBUG  [qsub_run_finished] : 
   Job 148011 for run 148011 finished 

  |                                                                            
  |======================================================================| 100%> 
> # Get results of model runs
> if (PEcAn.utils::status.check("OUTPUT") == 0) {
+   PEcAn.utils::status.start("OUTPUT")
+   runModule.get.results(settings)
+   PEcAn.utils::status.end()
+ }
Warning: read.ensemble.output has been moved to PEcAn.uncertainty and is deprecated from PEcAn.utils.
Please use PEcAn.uncertainty::read.ensemble.output instead.
PEcAn.utils::read.ensemble.output will not be updated and will be removed from a future version of PEcAn.
2018-08-17 11:02:32 INFO   [read.ensemble.output] : 
   reading ensemble output from run id: 2000075060 
2018-08-17 11:02:32 ERROR  [read.output] : 
   start.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:32 ERROR  [read.output] : 
   end.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 WARN   [read.output] : 
   read.output: no netCDF files of model output present for runid = 
   2000075060 in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075060 for 
   years 2004 : 2004 . will return NA 
2018-08-17 11:02:33 INFO   [read.output] : 
   NPP Mean: NaN Median: NA 
Warning in mean.default(out, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
2018-08-17 11:02:33 INFO   [read.ensemble.output] : 
   reading ensemble output from run id: 2000075061 
2018-08-17 11:02:33 ERROR  [read.output] : 
   start.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 ERROR  [read.output] : 
   end.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 WARN   [read.output] : 
   read.output: no netCDF files of model output present for runid = 
   2000075061 in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075061 for 
   years 2004 : 2004 . will return NA 
2018-08-17 11:02:33 INFO   [read.output] : 
   NPP Mean: NaN Median: NA 
Warning in mean.default(out, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
2018-08-17 11:02:33 INFO   [read.ensemble.output] : 
   reading ensemble output from run id: 2000075062 
2018-08-17 11:02:33 ERROR  [read.output] : 
   start.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 ERROR  [read.output] : 
   end.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 WARN   [read.output] : 
   read.output: no netCDF files of model output present for runid = 
   2000075062 in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075062 for 
   years 2004 : 2004 . will return NA 
2018-08-17 11:02:33 INFO   [read.output] : 
   NPP Mean: NaN Median: NA 
Warning in mean.default(out, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
2018-08-17 11:02:33 INFO   [read.ensemble.output] : 
   reading ensemble output from run id: 2000075063 
2018-08-17 11:02:33 ERROR  [read.output] : 
   start.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 ERROR  [read.output] : 
   end.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 WARN   [read.output] : 
   read.output: no netCDF files of model output present for runid = 
   2000075063 in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075063 for 
   years 2004 : 2004 . will return NA 
2018-08-17 11:02:33 INFO   [read.output] : 
   NPP Mean: NaN Median: NA 
Warning in mean.default(out, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
2018-08-17 11:02:33 INFO   [read.ensemble.output] : 
   reading ensemble output from run id: 2000075064 
2018-08-17 11:02:33 ERROR  [read.output] : 
   start.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 ERROR  [read.output] : 
   end.year must be of type numeric, character, Date, or POSIXt 
2018-08-17 11:02:33 WARN   [read.output] : 
   read.output: no netCDF files of model output present for runid = 
   2000075064 in 
   /data/Model_Output/pecan.output/PEcAn_2000000989/out/2000075064 for 
   years 2004 : 2004 . will return NA 
2018-08-17 11:02:33 INFO   [read.output] : 
   NPP Mean: NaN Median: NA 
Warning in mean.default(out, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
> 
> # Run ensemble analysis on model output. 
> if ('ensemble' %in% names(settings) & PEcAn.utils::status.check("ENSEMBLE") == 0) {
+   PEcAn.utils::status.start("ENSEMBLE")
+   runModule.run.ensemble.analysis(settings, TRUE)    
+   PEcAn.utils::status.end()
+ }
[1] "----- Variable: NPP"
2018-08-17 11:02:33 WARN   [run.ensemble.analysis] : 
   no data in ensemble.output 
> 
> # Run sensitivity analysis and variance decomposition on model output
> if ('sensitivity.analysis' %in% names(settings) & PEcAn.utils::status.check("SENSITIVITY") == 0) {
+   PEcAn.utils::status.start("SENSITIVITY")
+   runModule.run.sensitivity.analysis(settings)
+   PEcAn.utils::status.end()
+ }
> 
> # Run parameter data assimilation
> if ('assim.batch' %in% names(settings)) {
+   if (PEcAn.utils::status.check("PDA") == 0) {
+     PEcAn.utils::status.start("PDA")
+     settings <- PEcAn.assim.batch::runModule.assim.batch(settings)
+     PEcAn.utils::status.end()
+   }
+ }
> 
> # Run state data assimilation
> if ('state.data.assimilation' %in% names(settings)) {
+   if (PEcAn.utils::status.check("SDA") == 0) {
+     PEcAn.utils::status.start("SDA")
+     settings <- sda.enfk(settings)
+     PEcAn.utils::status.end()
+   }
+ }
> 
> # Run benchmarking
> if("benchmarking" %in% names(settings) & "benchmark" %in% names(settings$benchmarking)){
+   PEcAn.utils::status.start("BENCHMARKING")
+   results <- papply(settings, function(x) calc_benchmark(x, bety))
+   PEcAn.utils::status.end()
+ }
> 
> # Pecan workflow complete
> if (PEcAn.utils::status.check("FINISHED") == 0) {
+   PEcAn.utils::status.start("FINISHED")
+   PEcAn.remote::kill.tunnel(settings)
+   db.query(paste("UPDATE workflows SET finished_at=NOW() WHERE id=", settings$workflow$id, "AND finished_at IS NULL"), params=settings$database$bety)
+   
+   # Send email if configured
+   if (!is.null(settings$email) && !is.null(settings$email$to) && (settings$email$to != "")) {
+     sendmail(settings$email$from, settings$email$to,
+              paste0("Workflow has finished executing at ", base::date()),
+              paste0("You can find the results on ", settings$email$url))
+   }
+   PEcAn.utils::status.end()
+ }
> 
> db.print.connections()
2018-08-17 11:02:33 INFO   [db.print.connections] : 
   Created 12 connections and executed 181 queries 
2018-08-17 11:02:33 INFO   [db.print.connections] : 
   Created 12 connections and executed 181 queries 
2018-08-17 11:02:33 DEBUG  [db.print.connections] : 
   No open database connections. 
> print("---------- PEcAn Workflow Complete ----------")
[1] "---------- PEcAn Workflow Complete ----------"
> 
> proc.time()
   user  system elapsed 
 25.362   3.936 115.564 

As a note, this was using BrownDog and for whatever reason the data coming back either resulted in an empty sipnet.clim file or one full of gibberish

To Reproduce Steps to reproduce the behavior:

1. Run at site CZ3
2. Use SIPNET (r136)
3. Run with NARR (BrownDog) for any year
4. See error

Expected behavior An appropriate sipnet.clim file is created and the model uses this met driver and runs, creating SIPNET output

Screenshots none

Machine (please complete the following information):

• Server BNL/modex
• OS: Linux
• Browser(if applicable) chrome
• Version unknown

Additional context none

[github-actions[bot]]

This issue is stale because it has been open 365 days with no activity.